Preparation

Clean the environment.

Set locations, and the working directory.

A package-installation function.

Load those packages.

We will create a datestamp and define the Utrecht Science Park Colour Scheme.

# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    tvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    R = summary(fit)$r.squared
    R.adj = summary(fit)$adj.r.squared
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
    
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("T-value...................:", round(tvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("R^2.......................:", round(R, 6), "\n")
    cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,9))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data...\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    zvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    dev <- fit$deviance
    nullDev <- fit$null.deviance
    modelN <- length(fit$fitted.values)
    R.l <- 1 - dev / nullDev
    R.cs <- 1 - exp(-(nullDev - dev) / modelN)
    R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("Z-value...................:", round(zvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
    cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
    cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b). While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.

Objectives

Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.

Methods

Blood

OLINK-platform

  • IL6: Interleukin 6. Entrez Gene: 3569. OLINK, plasma
  • MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. OLINK, plasma

THESE DATA ARE NOT AVAILABLE YET

Plaque

Luminex-platform, measured by Luminex

  • MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug]. Measured in two experiments, variables MCP1 and MCP1_pg_ug_2015. We consider the latter the best possible measurement as this was corrected for plaque total protein concentration.
  • IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug].
  • IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug].

FACS platform

  • IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]

Loading data

Clinical data

Loading Athero-Express clinical data.

require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))

head(AEDB)
NA
NA
NA

Examine AEDB

We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is.

There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/.

AEDB %>% sjPlot::view_df(show.type = TRUE,
                         show.frq = TRUE,
                         show.prc = TRUE,
                         show.na = TRUE, 
                         max.len = TRUE, 
                         wrap.labels = 20,
                         verbose = FALSE, 
                         use.viewer = FALSE,
                         file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) 

Fixing and creating variables

We need to be very strict in defining symptoms. Therefore we will fix a new variable that groups symptoms at inclusion.

Coding of symptoms is as follows:

  • missing -999
  • Asymptomatic 0
  • TIA 1
  • minor stroke 2
  • Major stroke 3
  • Amaurosis fugax 4
  • Four vessel disease 5
  • Vertebrobasilary TIA 7
  • Retinal infarction 8
  • Symptomatic, but aspecific symtoms 9
  • Contralateral symptomatic occlusion 10
  • retinal infarction 11
  • armclaudication due to occlusion subclavian artery, CEA needed for bypass 12
  • retinal infarction + TIAs 13
  • Ocular ischemic syndrome 14
  • ischemisch glaucoom 15
  • subclavian steal syndrome 16
  • TGA 17

We will group as follows in Symptoms.5G:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13
  3. Stroke > 2, 3
  4. Ocular > 4, 14, 15
  5. Retinal infarction > 8, 11
  6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in AsymptSympt:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13 + Stroke > 2, 3
  3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17

We will also group as follows in AsymptSympt2G:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
# Fix symptoms

attach(AEDB)

AEDB$sympt[is.na(AEDB$sympt)] <- -999

# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")
              
               Asymptomatic Ocular Other Retinal infarction Stroke  TIA <NA>
  Asymptomatic          333      0     0                  0      0    0    0
  Symptomatic             0    416   119                 43    732 1045    0
  <NA>                    0      0     0                  0      0    0 1103
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

We will also fix the plaquephenotypes variable.

Coding of symptoms is as follows:

  • missing -999
  • not relevant -888
  • fibrous 1
  • fibroatheromatous 2
  • atheromatous 3

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

table(AEDB$OverallPlaquePhenotype)

     atheromatous fibroatheromatous           fibrous 
              550               841              1439 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the diabetes status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.

# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

table(AEDB$DM.composite)

   0    1 
2764  985 
table(AEDB$DiabetesStatus)

Control (no Diabetes Dx/Med)                     Diabetes 
                        2764                          985 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the smoking status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire.

  • diet801: are you a smoker?
  • diet802: did you smoke in the past?

We already have some variables indicating smoking status:

  • SmokingReported: patient has reported to smoke.
  • SmokingYearOR: smoking in the year of surgery?
  • SmokerCurrent: currently smoking?
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")

Fixing smoking status.
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")

* Current smoking status.
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))
Current smoker
no data available/missing                        no                       yes                      <NA> 
                        0                      2364                      1308                       119 
cat("\n* Updated smoking status.\n")

* Updated smoking status.
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))
Updated smoking status
Current smoker      Ex-smoker   Never smoked           <NA> 
          1308           1814            389            280 
cat("\n* Comparing to 'SmokerCurrent'.\n")

* Comparing to 'SmokerCurrent'.
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))
                      Current smoker
Updated smoking status no data available/missing   no  yes <NA>
        Current smoker                         0    0 1308    0
        Ex-smoker                              0 1814    0    0
        Never smoked                           0  389    0    0
        <NA>                                   0  161    0  119
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the alcohol status variable.


# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

table(AEDB$AlcoholUse)

  No  Yes 
1238 2345 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on CAD_history, Stroke_history, and Peripheral.interv


# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)

table(AEDB$CAD_history)

   0    1 
2430 1285 
table(AEDB$Stroke_history)

   0    1 
2763  947 
table(AEDB$Peripheral.interv)

   0    1 
2579 1099 
table(AEDB$MedHx_CVD)

  No  yes 
1309 2475 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

Athero-Express Biobank Study

Baseline characteristics

We are interested in the following variables at baseline.

  • Age (years)
  • Female sex (N, %)
  • Hypertension (N, %)
  • SBP (mmHg)
  • DBP (mmHg)
  • Diabetes mellitus (N, %)
  • Total cholesterol levels (mg/dL)
  • LDL cholesterol levels (mg/dL)
  • HDL cholesterol levels (mg/dL)
  • Triglyceride levels (mg/dL)
  • Use of statins (N, %)
  • Use of antiplatelet drugs (N, %)
  • BMI (kg/m²)
  • Smoking status (N, %)
    • Never smokers
    • Ex-smokers
    • Current smokers
  • History of CAD (N, %)
  • History of PAD (N, %)
  • Clinical manifestations
    • Asymptomatic
    • Amaurosis fugax
    • TIA
    • Stroke
  • eGFR (mL/min/1.73 m²)
  • MCP-1 plaque levels (pg/mL) (LUMINEX based, two experiments MCP1, and MCP1_pg_ug_2015)
  • MCP-1 serum levels (pg/mL) (OLINK based)
cat("===========================================================================================\n")
===========================================================================================
cat("CREATE BASELINE TABLE\n")
CREATE BASELINE TABLE
# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "restenos", "stenose",
                   "MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6", "IL6_pg_ug_2015", "IL6R_pg_ug_2015",
                   "MCP1", "MCP1_pg_ug_2015")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con

All patients

Showing the baseline table of the whole Athero-Express Biobank.

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
                                      
                                       level                                                                     Overall           Missing
  n                                                                                                                 3791                  
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     45.8 (1735)     0.0   
                                       UMC Utrecht                                                                  54.2 (2056)           
  ORyear % (freq)                      No data available/missing                                                     0.0 (   0)     0.0   
                                       2002                                                                          2.5 (  94)           
                                       2003                                                                          5.4 ( 204)           
                                       2004                                                                          7.6 ( 289)           
                                       2005                                                                          8.2 ( 309)           
                                       2006                                                                          7.5 ( 285)           
                                       2007                                                                          6.2 ( 234)           
                                       2008                                                                          5.9 ( 223)           
                                       2009                                                                          7.0 ( 267)           
                                       2010                                                                          8.1 ( 307)           
                                       2011                                                                          7.1 ( 269)           
                                       2012                                                                          8.2 ( 312)           
                                       2013                                                                          6.9 ( 262)           
                                       2014                                                                          7.9 ( 299)           
                                       2015                                                                          2.1 (  79)           
                                       2016                                                                          3.3 ( 124)           
                                       2017                                                                          2.2 (  85)           
                                       2018                                                                          2.1 (  80)           
                                       2019                                                                          1.8 (  69)           
  Age (mean (SD))                                                                                                 68.907 (9.322)    0.0   
  Gender % (freq)                      female                                                                       30.6 (1160)     0.0   
                                       male                                                                         69.4 (2631)           
  TC_finalCU (mean (SD))                                                                                         185.220 (81.513)  46.8   
  LDL_finalCU (mean (SD))                                                                                        106.483 (40.683)  54.5   
  HDL_finalCU (mean (SD))                                                                                         46.593 (16.730)  51.1   
  TG_finalCU (mean (SD))                                                                                         154.233 (99.797)  51.8   
  TC_final (mean (SD))                                                                                             4.797 (2.111)   46.8   
  LDL_final (mean (SD))                                                                                            2.758 (1.054)   54.5   
  HDL_final (mean (SD))                                                                                            1.207 (0.433)   51.1   
  TG_final (mean (SD))                                                                                             1.743 (1.128)   51.8   
  hsCRP_plasma (mean (SD))                                                                                        19.250 (206.888) 60.7   
  systolic (mean (SD))                                                                                           150.907 (25.117)  13.5   
  diastoli (mean (SD))                                                                                            79.934 (21.853)  13.5   
  GFR_MDRD (mean (SD))                                                                                            74.849 (24.745)   6.5   
  BMI (mean (SD))                                                                                                 26.336 (4.051)    7.5   
  KDOQI % (freq)                       No data available/missing                                                     0.0 (   0)     6.6   
                                       Normal kidney function                                                       22.1 ( 839)           
                                       CKD 2 (Mild)                                                                 47.2 (1788)           
                                       CKD 3 (Moderate)                                                             21.9 ( 830)           
                                       CKD 4 (Severe)                                                                1.4 (  53)           
                                       CKD 5 (Failure)                                                               0.8 (  32)           
                                       <NA>                                                                          6.6 ( 249)           
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (   0)     7.5   
                                       Underweight                                                                   1.2 (  44)           
                                       Normal                                                                       35.2 (1335)           
                                       Overweight                                                                   42.0 (1594)           
                                       Obese                                                                        14.1 ( 533)           
                                       <NA>                                                                          7.5 ( 285)           
  SmokerStatus % (freq)                Current smoker                                                               34.5 (1308)     7.4   
                                       Ex-smoker                                                                    47.9 (1814)           
                                       Never smoked                                                                 10.3 ( 389)           
                                       <NA>                                                                          7.4 ( 280)           
  AlcoholUse % (freq)                  No                                                                           32.7 (1238)     5.5   
                                       Yes                                                                          61.9 (2345)           
                                       <NA>                                                                          5.5 ( 208)           
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 72.9 (2764)     1.1   
                                       Diabetes                                                                     26.0 ( 985)           
                                       <NA>                                                                          1.1 (  42)           
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (   0)     4.0   
                                       no                                                                           23.7 ( 899)           
                                       yes                                                                          72.3 (2741)           
                                       <NA>                                                                          4.0 ( 151)           
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (   0)     5.5   
                                       no                                                                           28.6 (1085)           
                                       yes                                                                          65.9 (2499)           
                                       <NA>                                                                          5.5 ( 207)           
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (   0)     1.3   
                                       no                                                                           13.3 ( 504)           
                                       yes                                                                          85.4 (3239)           
                                       <NA>                                                                          1.3 (  48)           
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (   0)     1.5   
                                       no                                                                           21.0 ( 797)           
                                       yes                                                                          77.5 (2939)           
                                       <NA>                                                                          1.5 (  55)           
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (   0)     1.6   
                                       no                                                                           85.6 (3246)           
                                       yes                                                                          12.8 ( 485)           
                                       <NA>                                                                          1.6 (  60)           
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (   0)     1.6   
                                       no                                                                           13.7 ( 521)           
                                       yes                                                                          84.7 (3211)           
                                       <NA>                                                                          1.6 (  59)           
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (   0)     1.5   
                                       no                                                                           21.8 ( 826)           
                                       yes                                                                          76.7 (2909)           
                                       <NA>                                                                          1.5 (  56)           
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (   0)     8.1   
                                       No stroke diagnosed                                                          74.4 (2822)           
                                       Stroke diagnosed                                                             17.5 ( 662)           
                                       <NA>                                                                          8.1 ( 307)           
  sympt % (freq)                       missing                                                                      29.1 (1103)     0.0   
                                       Asymptomatic                                                                  8.8 ( 333)           
                                       TIA                                                                          27.4 (1040)           
                                       minor stroke                                                                 12.1 ( 458)           
                                       Major stroke                                                                  7.2 ( 274)           
                                       Amaurosis fugax                                                              10.5 ( 398)           
                                       Four vessel disease                                                           1.1 (  43)           
                                       Vertebrobasilary TIA                                                          0.1 (   5)           
                                       Retinal infarction                                                            1.0 (  37)           
                                       Symptomatic, but aspecific symtoms                                            1.6 (  61)           
                                       Contralateral symptomatic occlusion                                           0.3 (  12)           
                                       retinal infarction                                                            0.2 (   6)           
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (   1)           
                                       retinal infarction + TIAs                                                     0.0 (   0)           
                                       Ocular ischemic syndrome                                                      0.5 (  18)           
                                       ischemisch glaucoom                                                           0.0 (   0)           
                                       subclavian steal syndrome                                                     0.1 (   2)           
                                       TGA                                                                           0.0 (   0)           
  Symptoms.5G % (freq)                 Asymptomatic                                                                  8.8 ( 333)    29.1   
                                       Ocular                                                                       11.0 ( 416)           
                                       Other                                                                         3.1 ( 119)           
                                       Retinal infarction                                                            1.1 (  43)           
                                       Stroke                                                                       19.3 ( 732)           
                                       TIA                                                                          27.6 (1045)           
                                       <NA>                                                                         29.1 (1103)           
  AsymptSympt % (freq)                 Asymptomatic                                                                  8.8 ( 333)    29.1   
                                       Ocular and others                                                            15.2 ( 578)           
                                       Symptomatic                                                                  46.9 (1777)           
                                       <NA>                                                                         29.1 (1103)           
  AsymptSympt2G % (freq)               Asymptomatic                                                                  8.8 ( 333)    29.1   
                                       Symptomatic                                                                  62.1 (2355)           
                                       <NA>                                                                         29.1 (1103)           
  restenos % (freq)                    missing                                                                       0.0 (   0)     4.0   
                                       de novo                                                                      87.0 (3297)           
                                       restenosis                                                                    8.8 ( 334)           
                                       stenose bij angioseal na PTCA                                                 0.2 (   7)           
                                       <NA>                                                                          4.0 ( 153)           
  stenose % (freq)                     missing                                                                       0.0 (   0)     7.0   
                                       0-49%                                                                         0.7 (  25)           
                                       50-70%                                                                        6.8 ( 256)           
                                       70-90%                                                                       35.6 (1349)           
                                       90-99%                                                                       29.9 (1132)           
                                       100% (Occlusion)                                                             14.8 ( 560)           
                                       NA                                                                            0.1 (   3)           
                                       50-99%                                                                        2.6 (  99)           
                                       70-99%                                                                        2.6 ( 100)           
                                       99                                                                            0.1 (   2)           
                                       <NA>                                                                          7.0 ( 265)           
  MedHx_CVD % (freq)                   No                                                                           34.5 (1309)     0.2   
                                       yes                                                                          65.3 (2475)           
                                       <NA>                                                                          0.2 (   7)           
  CAD_history % (freq)                 Missing                                                                       0.0 (   0)     2.0   
                                       No history CAD                                                               64.1 (2430)           
                                       History CAD                                                                  33.9 (1285)           
                                       <NA>                                                                          2.0 (  76)           
  PAOD % (freq)                        missing/no data                                                               0.0 (   0)     1.6   
                                       no                                                                           55.1 (2088)           
                                       yes                                                                          43.4 (1644)           
                                       <NA>                                                                          1.6 (  59)           
  Peripheral.interv % (freq)           no                                                                           68.0 (2579)     3.0   
                                       yes                                                                          29.0 (1099)           
                                       <NA>                                                                          3.0 ( 113)           
  EP_composite % (freq)                No data available.                                                            0.0 (   0)     7.3   
                                       No composite endpoints                                                       60.6 (2297)           
                                       Composite endpoints                                                          32.1 (1218)           
                                       <NA>                                                                          7.3 ( 276)           
  EP_composite_time (mean (SD))                                                                                    2.266 (1.203)    7.4   
  macmean0 (mean (SD))                                                                                             0.656 (1.154)   32.4   
  smcmean0 (mean (SD))                                                                                             2.291 (6.620)   32.4   
  Macrophages.bin % (freq)             no/minor                                                                     42.3 (1602)    25.7   
                                       moderate/heavy                                                               32.0 (1215)           
                                       <NA>                                                                         25.7 ( 974)           
  SMC.bin % (freq)                     no/minor                                                                     22.9 ( 870)    25.3   
                                       moderate/heavy                                                               51.8 (1962)           
                                       <NA>                                                                         25.3 ( 959)           
  neutrophils (mean (SD))                                                                                        162.985 (490.469) 91.0   
  Mast_cells_plaque (mean (SD))                                                                                  165.663 (163.421) 93.0   
  IPH.bin % (freq)                     no                                                                           32.3 (1223)    24.8   
                                       yes                                                                          42.9 (1628)           
                                       <NA>                                                                         24.8 ( 940)           
  vessel_density_averaged (mean (SD))                                                                              8.030 (6.348)   48.0   
  Calc.bin % (freq)                    no/minor                                                                     37.9 (1437)    24.7   
                                       moderate/heavy                                                               37.4 (1416)           
                                       <NA>                                                                         24.7 ( 938)           
  Collagen.bin % (freq)                no/minor                                                                     14.2 ( 540)    25.2   
                                       moderate/heavy                                                               60.6 (2297)           
                                       <NA>                                                                         25.2 ( 954)           
  Fat.bin_10 % (freq)                   <10%                                                                        32.3 (1226)    24.7   
                                        >10%                                                                        42.9 (1628)           
                                       <NA>                                                                         24.7 ( 937)           
  Fat.bin_40 % (freq)                  <40%                                                                         60.0 (2274)    24.7   
                                       >40%                                                                         15.3 ( 580)           
                                       <NA>                                                                         24.7 ( 937)           
  OverallPlaquePhenotype % (freq)      atheromatous                                                                 14.5 ( 550)    25.3   
                                       fibroatheromatous                                                            22.2 ( 841)           
                                       fibrous                                                                      38.0 (1439)           
                                       <NA>                                                                         25.3 ( 961)           
  IL6 (mean (SD))                                                                                                 94.451 (278.490) 84.5   
  IL6_pg_ug_2015 (mean (SD))                                                                                       0.135 (0.541)   67.2   
  IL6R_pg_ug_2015 (mean (SD))                                                                                      0.212 (0.251)   67.1   
  MCP1 (mean (SD))                                                                                               130.926 (118.422) 83.7   
  MCP1_pg_ug_2015 (mean (SD))                                                                                      0.596 (0.880)   65.5   

CEA patients

Showing the baseline table of the CEA patients in the Athero-Express Biobank.

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
                                      
                                       level                                                                     Overall           Missing
  n                                                                                                                 2421                  
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     39.2 ( 948)     0.0   
                                       UMC Utrecht                                                                  60.8 (1473)           
  ORyear % (freq)                      No data available/missing                                                     0.0 (   0)     0.0   
                                       2002                                                                          3.3 (  81)           
                                       2003                                                                          6.5 ( 157)           
                                       2004                                                                          7.8 ( 190)           
                                       2005                                                                          7.6 ( 185)           
                                       2006                                                                          7.6 ( 183)           
                                       2007                                                                          6.3 ( 152)           
                                       2008                                                                          5.7 ( 138)           
                                       2009                                                                          7.5 ( 181)           
                                       2010                                                                          6.6 ( 159)           
                                       2011                                                                          6.7 ( 163)           
                                       2012                                                                          7.3 ( 176)           
                                       2013                                                                          6.2 ( 149)           
                                       2014                                                                          6.7 ( 163)           
                                       2015                                                                          3.1 (  76)           
                                       2016                                                                          3.5 (  85)           
                                       2017                                                                          2.7 (  65)           
                                       2018                                                                          2.7 (  66)           
                                       2019                                                                          2.1 (  52)           
  Age (mean (SD))                                                                                                 69.105 (9.302)    0.0   
  Gender % (freq)                      female                                                                       30.5 ( 738)     0.0   
                                       male                                                                         69.5 (1683)           
  TC_finalCU (mean (SD))                                                                                         184.803 (56.262)  38.0   
  LDL_finalCU (mean (SD))                                                                                        108.420 (41.744)  45.6   
  HDL_finalCU (mean (SD))                                                                                         46.435 (17.005)  41.7   
  TG_finalCU (mean (SD))                                                                                         151.216 (91.277)  42.8   
  TC_final (mean (SD))                                                                                             4.786 (1.457)   38.0   
  LDL_final (mean (SD))                                                                                            2.808 (1.081)   45.6   
  HDL_final (mean (SD))                                                                                            1.203 (0.440)   41.7   
  TG_final (mean (SD))                                                                                             1.709 (1.031)   42.8   
  hsCRP_plasma (mean (SD))                                                                                        19.914 (231.655) 53.0   
  systolic (mean (SD))                                                                                           152.419 (25.166)  11.3   
  diastoli (mean (SD))                                                                                            81.318 (25.188)  11.3   
  GFR_MDRD (mean (SD))                                                                                            73.121 (21.152)   5.4   
  BMI (mean (SD))                                                                                                 26.488 (3.977)    5.9   
  KDOQI % (freq)                       No data available/missing                                                     0.0 (   0)     5.5   
                                       Normal kidney function                                                       19.1 ( 462)           
                                       CKD 2 (Mild)                                                                 50.9 (1232)           
                                       CKD 3 (Moderate)                                                             22.8 ( 553)           
                                       CKD 4 (Severe)                                                                1.3 (  32)           
                                       CKD 5 (Failure)                                                               0.4 (  10)           
                                       <NA>                                                                          5.5 ( 132)           
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (   0)     5.9   
                                       Underweight                                                                   1.0 (  24)           
                                       Normal                                                                       35.1 ( 850)           
                                       Overweight                                                                   43.4 (1051)           
                                       Obese                                                                        14.5 ( 352)           
                                       <NA>                                                                          5.9 ( 144)           
  SmokerStatus % (freq)                Current smoker                                                               33.2 ( 803)     5.9   
                                       Ex-smoker                                                                    48.0 (1163)           
                                       Never smoked                                                                 12.9 ( 313)           
                                       <NA>                                                                          5.9 ( 142)           
  AlcoholUse % (freq)                  No                                                                           34.5 ( 835)     4.0   
                                       Yes                                                                          61.5 (1488)           
                                       <NA>                                                                          4.0 (  98)           
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 75.2 (1820)     1.1   
                                       Diabetes                                                                     23.7 ( 574)           
                                       <NA>                                                                          1.1 (  27)           
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (   0)     3.2   
                                       no                                                                           24.3 ( 589)           
                                       yes                                                                          72.4 (1754)           
                                       <NA>                                                                          3.2 (  78)           
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (   0)     4.4   
                                       no                                                                           29.9 ( 725)           
                                       yes                                                                          65.6 (1589)           
                                       <NA>                                                                          4.4 ( 107)           
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (   0)     1.2   
                                       no                                                                           14.6 ( 353)           
                                       yes                                                                          84.3 (2040)           
                                       <NA>                                                                          1.2 (  28)           
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (   0)     1.4   
                                       no                                                                           23.3 ( 565)           
                                       yes                                                                          75.3 (1823)           
                                       <NA>                                                                          1.4 (  33)           
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (   0)     1.6   
                                       no                                                                           87.3 (2114)           
                                       yes                                                                          11.1 ( 269)           
                                       <NA>                                                                          1.6 (  38)           
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (   0)     1.5   
                                       no                                                                           12.2 ( 295)           
                                       yes                                                                          86.3 (2090)           
                                       <NA>                                                                          1.5 (  36)           
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (   0)     1.4   
                                       no                                                                           20.3 ( 491)           
                                       yes                                                                          78.3 (1896)           
                                       <NA>                                                                          1.4 (  34)           
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (   0)     6.9   
                                       No stroke diagnosed                                                          71.5 (1731)           
                                       Stroke diagnosed                                                             21.6 ( 524)           
                                       <NA>                                                                          6.9 ( 166)           
  sympt % (freq)                       missing                                                                       0.0 (   0)     0.0   
                                       Asymptomatic                                                                 11.2 ( 270)           
                                       TIA                                                                          39.7 ( 961)           
                                       minor stroke                                                                 16.8 ( 407)           
                                       Major stroke                                                                  9.8 ( 238)           
                                       Amaurosis fugax                                                              15.7 ( 379)           
                                       Four vessel disease                                                           1.6 (  38)           
                                       Vertebrobasilary TIA                                                          0.2 (   5)           
                                       Retinal infarction                                                            1.4 (  34)           
                                       Symptomatic, but aspecific symtoms                                            2.2 (  53)           
                                       Contralateral symptomatic occlusion                                           0.5 (  11)           
                                       retinal infarction                                                            0.2 (   6)           
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (   1)           
                                       retinal infarction + TIAs                                                     0.0 (   0)           
                                       Ocular ischemic syndrome                                                      0.7 (  16)           
                                       ischemisch glaucoom                                                           0.0 (   0)           
                                       subclavian steal syndrome                                                     0.1 (   2)           
                                       TGA                                                                           0.0 (   0)           
  Symptoms.5G % (freq)                 Asymptomatic                                                                 11.2 ( 270)     0.0   
                                       Ocular                                                                       16.3 ( 395)           
                                       Other                                                                         4.3 ( 105)           
                                       Retinal infarction                                                            1.7 (  40)           
                                       Stroke                                                                       26.6 ( 645)           
                                       TIA                                                                          39.9 ( 966)           
  AsymptSympt % (freq)                 Asymptomatic                                                                 11.2 ( 270)     0.0   
                                       Ocular and others                                                            22.3 ( 540)           
                                       Symptomatic                                                                  66.5 (1611)           
  AsymptSympt2G % (freq)               Asymptomatic                                                                 11.2 ( 270)     0.0   
                                       Symptomatic                                                                  88.8 (2151)           
  restenos % (freq)                    missing                                                                       0.0 (   0)     1.4   
                                       de novo                                                                      93.7 (2268)           
                                       restenosis                                                                    4.9 ( 118)           
                                       stenose bij angioseal na PTCA                                                 0.0 (   0)           
                                       <NA>                                                                          1.4 (  35)           
  stenose % (freq)                     missing                                                                       0.0 (   0)     2.0   
                                       0-49%                                                                         0.5 (  13)           
                                       50-70%                                                                        7.8 ( 189)           
                                       70-90%                                                                       46.6 (1127)           
                                       90-99%                                                                       38.3 ( 927)           
                                       100% (Occlusion)                                                              1.3 (  31)           
                                       NA                                                                            0.0 (   1)           
                                       50-99%                                                                        0.6 (  15)           
                                       70-99%                                                                        2.8 (  68)           
                                       99                                                                            0.1 (   2)           
                                       <NA>                                                                          2.0 (  48)           
  MedHx_CVD % (freq)                   No                                                                           36.8 ( 892)     0.0   
                                       yes                                                                          63.2 (1529)           
  CAD_history % (freq)                 Missing                                                                       0.0 (   0)     1.9   
                                       No history CAD                                                               66.8 (1618)           
                                       History CAD                                                                  31.2 ( 756)           
                                       <NA>                                                                          1.9 (  47)           
  PAOD % (freq)                        missing/no data                                                               0.0 (   0)     2.0   
                                       no                                                                           77.5 (1876)           
                                       yes                                                                          20.5 ( 497)           
                                       <NA>                                                                          2.0 (  48)           
  Peripheral.interv % (freq)           no                                                                           77.2 (1868)     2.9   
                                       yes                                                                          19.9 ( 482)           
                                       <NA>                                                                          2.9 (  71)           
  EP_composite % (freq)                No data available.                                                            0.0 (   0)     5.0   
                                       No composite endpoints                                                       70.6 (1709)           
                                       Composite endpoints                                                          24.4 ( 590)           
                                       <NA>                                                                          5.0 ( 122)           
  EP_composite_time (mean (SD))                                                                                    2.479 (1.109)    5.2   
  macmean0 (mean (SD))                                                                                             0.768 (1.184)   29.7   
  smcmean0 (mean (SD))                                                                                             1.985 (2.381)   29.9   
  Macrophages.bin % (freq)             no/minor                                                                     34.9 ( 846)    24.1   
                                       moderate/heavy                                                               40.9 ( 991)           
                                       <NA>                                                                         24.1 ( 584)           
  SMC.bin % (freq)                     no/minor                                                                     24.9 ( 602)    23.8   
                                       moderate/heavy                                                               51.3 (1242)           
                                       <NA>                                                                         23.8 ( 577)           
  neutrophils (mean (SD))                                                                                        147.151 (419.998) 87.4   
  Mast_cells_plaque (mean (SD))                                                                                  164.488 (163.771) 90.0   
  IPH.bin % (freq)                     no                                                                           30.7 ( 744)    23.5   
                                       yes                                                                          45.8 (1108)           
                                       <NA>                                                                         23.5 ( 569)           
  vessel_density_averaged (mean (SD))                                                                              8.318 (6.388)   35.1   
  Calc.bin % (freq)                    no/minor                                                                     41.6 (1006)    23.4   
                                       moderate/heavy                                                               35.1 ( 849)           
                                       <NA>                                                                         23.4 ( 566)           
  Collagen.bin % (freq)                no/minor                                                                     15.8 ( 382)    23.6   
                                       moderate/heavy                                                               60.6 (1467)           
                                       <NA>                                                                         23.6 ( 572)           
  Fat.bin_10 % (freq)                   <10%                                                                        22.4 ( 542)    23.3   
                                        >10%                                                                        54.3 (1314)           
                                       <NA>                                                                         23.3 ( 565)           
  Fat.bin_40 % (freq)                  <40%                                                                         56.2 (1360)    23.3   
                                       >40%                                                                         20.5 ( 496)           
                                       <NA>                                                                         23.3 ( 565)           
  OverallPlaquePhenotype % (freq)      atheromatous                                                                 19.8 ( 480)    23.7   
                                       fibroatheromatous                                                            27.8 ( 672)           
                                       fibrous                                                                      28.7 ( 695)           
                                       <NA>                                                                         23.7 ( 574)           
  IL6 (mean (SD))                                                                                                 98.812 (292.457) 78.2   
  IL6_pg_ug_2015 (mean (SD))                                                                                       0.138 (0.556)   52.5   
  IL6R_pg_ug_2015 (mean (SD))                                                                                      0.212 (0.251)   52.4   
  MCP1 (mean (SD))                                                                                               135.763 (120.028) 76.7   
  MCP1_pg_ug_2015 (mean (SD))                                                                                      0.612 (0.905)   50.6   

CEA patients with MCP1_pg_ug_2015

Showing the baseline table of the CEA patients in the Athero-Express Biobank with MCP1_pg_ug_2015.

AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015))

AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
                                      Stratified by AsymptSympt2G
                                       level                                                                     Asymptomatic      Symptomatic       p      test Missing
  n                                                                                                                  131              1065                              
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     50.4 ( 66)        46.6 ( 496)     0.464       0.0   
                                       UMC Utrecht                                                                  49.6 ( 65)        53.4 ( 569)                       
  ORyear % (freq)                      No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       2002                                                                         10.7 ( 14)         3.9 (  42)                       
                                       2003                                                                          7.6 ( 10)         9.4 ( 100)                       
                                       2004                                                                         17.6 ( 23)        11.5 ( 123)                       
                                       2005                                                                          9.9 ( 13)        11.2 ( 119)                       
                                       2006                                                                         10.7 ( 14)        10.2 ( 109)                       
                                       2007                                                                         11.5 ( 15)        10.5 ( 112)                       
                                       2008                                                                          7.6 ( 10)         7.4 (  79)                       
                                       2009                                                                          7.6 ( 10)         8.4 (  89)                       
                                       2010                                                                          5.3 (  7)         7.6 (  81)                       
                                       2011                                                                          6.1 (  8)         9.5 ( 101)                       
                                       2012                                                                          5.3 (  7)         8.3 (  88)                       
                                       2013                                                                          0.0 (  0)         2.0 (  21)                       
                                       2014                                                                          0.0 (  0)         0.1 (   1)                       
                                       2015                                                                          0.0 (  0)         0.0 (   0)                       
                                       2016                                                                          0.0 (  0)         0.0 (   0)                       
                                       2017                                                                          0.0 (  0)         0.0 (   0)                       
                                       2018                                                                          0.0 (  0)         0.0 (   0)                       
                                       2019                                                                          0.0 (  0)         0.0 (   0)                       
  Age (mean (SD))                                                                                                 66.237 (9.184)    68.941 (9.119)    0.001       0.0   
  Gender % (freq)                      female                                                                       23.7 ( 31)        31.4 ( 334)     0.088       0.0   
                                       male                                                                         76.3 (100)        68.6 ( 731)                       
  TC_finalCU (mean (SD))                                                                                         175.987 (47.184)  183.420 (48.377)   0.180      33.4   
  LDL_finalCU (mean (SD))                                                                                        102.781 (38.324)  109.247 (41.008)   0.191      39.6   
  HDL_finalCU (mean (SD))                                                                                         43.701 (14.754)   45.814 (18.526)   0.317      36.4   
  TG_finalCU (mean (SD))                                                                                         157.650 (89.246)  145.238 (84.872)   0.211      36.0   
  TC_final (mean (SD))                                                                                             4.558 (1.222)     4.751 (1.253)    0.180      33.4   
  LDL_final (mean (SD))                                                                                            2.662 (0.993)     2.829 (1.062)    0.191      39.6   
  HDL_final (mean (SD))                                                                                            1.132 (0.382)     1.187 (0.480)    0.317      36.4   
  TG_final (mean (SD))                                                                                             1.781 (1.008)     1.641 (0.959)    0.211      36.0   
  hsCRP_plasma (mean (SD))                                                                                         5.688 (19.440)   16.588 (113.882)  0.379      38.8   
  systolic (mean (SD))                                                                                           153.577 (24.327)  155.822 (26.180)   0.390      14.0   
  diastoli (mean (SD))                                                                                            80.622 (13.225)   82.894 (13.581)   0.095      14.0   
  GFR_MDRD (mean (SD))                                                                                            71.026 (20.424)   71.879 (20.071)   0.653       3.5   
  BMI (mean (SD))                                                                                                 26.623 (3.391)    26.321 (3.748)    0.383       4.2   
  KDOQI % (freq)                       No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         3.6   
                                       Normal kidney function                                                       17.6 ( 23)        17.3 ( 184)                       
                                       CKD 2 (Mild)                                                                 49.6 ( 65)        53.2 ( 567)                       
                                       CKD 3 (Moderate)                                                             28.2 ( 37)        24.3 ( 259)                       
                                       CKD 4 (Severe)                                                                0.0 (  0)         1.2 (  13)                       
                                       CKD 5 (Failure)                                                               0.8 (  1)         0.4 (   4)                       
                                       <NA>                                                                          3.8 (  5)         3.6 (  38)                       
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         4.3   
                                       Underweight                                                                   0.8 (  1)         0.9 (  10)                       
                                       Normal                                                                       32.8 ( 43)        35.6 ( 379)                       
                                       Overweight                                                                   51.1 ( 67)        46.1 ( 491)                       
                                       Obese                                                                        13.0 ( 17)        12.8 ( 136)                       
                                       <NA>                                                                          2.3 (  3)         4.6 (  49)                       
  SmokerStatus % (freq)                Current smoker                                                               30.5 ( 40)        36.2 ( 385)     0.077       3.8   
                                       Ex-smoker                                                                    57.3 ( 75)        45.6 ( 486)                       
                                       Never smoked                                                                  9.9 ( 13)        14.3 ( 152)                       
                                       <NA>                                                                          2.3 (  3)         3.9 (  42)                       
  AlcoholUse % (freq)                  No                                                                           38.2 ( 50)        33.3 ( 355)     0.359       4.0   
                                       Yes                                                                          59.5 ( 78)        62.4 ( 665)                       
                                       <NA>                                                                          2.3 (  3)         4.2 (  45)                       
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 76.3 (100)        77.4 ( 824)     0.876       0.0   
                                       Diabetes                                                                     23.7 ( 31)        22.6 ( 241)                       
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         2.0   
                                       no                                                                           23.7 ( 31)        26.6 ( 283)                       
                                       yes                                                                          75.6 ( 99)        71.3 ( 759)                       
                                       <NA>                                                                          0.8 (  1)         2.2 (  23)                       
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         2.7   
                                       no                                                                           30.5 ( 40)        32.9 ( 350)                       
                                       yes                                                                          67.9 ( 89)        64.3 ( 685)                       
                                       <NA>                                                                          1.5 (  2)         2.8 (  30)                       
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       no                                                                            9.9 ( 13)        14.3 ( 152)                       
                                       yes                                                                          90.1 (118)        85.7 ( 913)                       
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.2   
                                       no                                                                           14.5 ( 19)        23.3 ( 248)                       
                                       yes                                                                          85.5 (112)        76.5 ( 815)                       
                                       <NA>                                                                          0.0 (  0)         0.2 (   2)                       
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.2   
                                       no                                                                           89.3 (117)        88.0 ( 937)                       
                                       yes                                                                          10.7 ( 14)        11.8 ( 126)                       
                                       <NA>                                                                          0.0 (  0)         0.2 (   2)                       
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.4   
                                       no                                                                            6.1 (  8)        11.0 ( 117)                       
                                       yes                                                                          93.1 (122)        88.6 ( 944)                       
                                       <NA>                                                                          0.8 (  1)         0.4 (   4)                       
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.2   
                                       no                                                                           15.3 ( 20)        22.7 ( 242)                       
                                       yes                                                                          84.7 (111)        77.1 ( 821)                       
                                       <NA>                                                                          0.0 (  0)         0.2 (   2)                       
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (  0)         0.0 (   0)     NaN         5.4   
                                       No stroke diagnosed                                                          80.2 (105)        75.2 ( 801)                       
                                       Stroke diagnosed                                                             14.5 ( 19)        19.4 ( 207)                       
                                       <NA>                                                                          5.3 (  7)         5.4 (  57)                       
  sympt % (freq)                       missing                                                                       0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       Asymptomatic                                                                100.0 (131)         0.0 (   0)                       
                                       TIA                                                                           0.0 (  0)        46.4 ( 494)                       
                                       minor stroke                                                                  0.0 (  0)        16.7 ( 178)                       
                                       Major stroke                                                                  0.0 (  0)        12.2 ( 130)                       
                                       Amaurosis fugax                                                               0.0 (  0)        17.2 ( 183)                       
                                       Four vessel disease                                                           0.0 (  0)         2.2 (  23)                       
                                       Vertebrobasilary TIA                                                          0.0 (  0)         0.2 (   2)                       
                                       Retinal infarction                                                            0.0 (  0)         1.4 (  15)                       
                                       Symptomatic, but aspecific symtoms                                            0.0 (  0)         2.7 (  29)                       
                                       Contralateral symptomatic occlusion                                           0.0 (  0)         0.6 (   6)                       
                                       retinal infarction                                                            0.0 (  0)         0.3 (   3)                       
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (  0)         0.1 (   1)                       
                                       retinal infarction + TIAs                                                     0.0 (  0)         0.0 (   0)                       
                                       Ocular ischemic syndrome                                                      0.0 (  0)         0.1 (   1)                       
                                       ischemisch glaucoom                                                           0.0 (  0)         0.0 (   0)                       
                                       subclavian steal syndrome                                                     0.0 (  0)         0.0 (   0)                       
                                       TGA                                                                           0.0 (  0)         0.0 (   0)                       
  Symptoms.5G % (freq)                 Asymptomatic                                                                100.0 (131)         0.0 (   0)    <0.001       0.0   
                                       Ocular                                                                        0.0 (  0)        17.3 ( 184)                       
                                       Other                                                                         0.0 (  0)         5.5 (  59)                       
                                       Retinal infarction                                                            0.0 (  0)         1.7 (  18)                       
                                       Stroke                                                                        0.0 (  0)        28.9 ( 308)                       
                                       TIA                                                                           0.0 (  0)        46.6 ( 496)                       
  AsymptSympt % (freq)                 Asymptomatic                                                                100.0 (131)         0.0 (   0)    <0.001       0.0   
                                       Ocular and others                                                             0.0 (  0)        24.5 ( 261)                       
                                       Symptomatic                                                                   0.0 (  0)        75.5 ( 804)                       
  AsymptSympt2G % (freq)               Asymptomatic                                                                100.0 (131)         0.0 (   0)    <0.001       0.0   
                                       Symptomatic                                                                   0.0 (  0)       100.0 (1065)                       
  restenos % (freq)                    missing                                                                       0.0 (  0)         0.0 (   0)     NaN         2.3   
                                       de novo                                                                      93.9 (123)        94.7 (1009)                       
                                       restenosis                                                                    2.3 (  3)         3.2 (  34)                       
                                       stenose bij angioseal na PTCA                                                 0.0 (  0)         0.0 (   0)                       
                                       <NA>                                                                          3.8 (  5)         2.1 (  22)                       
  stenose % (freq)                     missing                                                                       0.0 (  0)         0.0 (   0)     NaN         3.2   
                                       0-49%                                                                         0.0 (  0)         0.6 (   6)                       
                                       50-70%                                                                        3.1 (  4)         6.4 (  68)                       
                                       70-90%                                                                       51.1 ( 67)        44.6 ( 475)                       
                                       90-99%                                                                       41.2 ( 54)        42.7 ( 455)                       
                                       100% (Occlusion)                                                              0.0 (  0)         0.9 (  10)                       
                                       NA                                                                            0.0 (  0)         0.0 (   0)                       
                                       50-99%                                                                        0.8 (  1)         0.4 (   4)                       
                                       70-99%                                                                        0.0 (  0)         1.3 (  14)                       
                                       99                                                                            0.0 (  0)         0.0 (   0)                       
                                       <NA>                                                                          3.8 (  5)         3.1 (  33)                       
  MedHx_CVD % (freq)                   No                                                                           38.9 ( 51)        36.9 ( 393)     0.720       0.0   
                                       yes                                                                          61.1 ( 80)        63.1 ( 672)                       
  CAD_history % (freq)                 Missing                                                                       0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       No history CAD                                                               61.8 ( 81)        69.9 ( 744)                       
                                       History CAD                                                                  38.2 ( 50)        30.1 ( 321)                       
  PAOD % (freq)                        missing/no data                                                               0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       no                                                                           74.0 ( 97)        79.5 ( 847)                       
                                       yes                                                                          26.0 ( 34)        20.5 ( 218)                       
  Peripheral.interv % (freq)           no                                                                           74.0 ( 97)        82.5 ( 879)     0.042       0.3   
                                       yes                                                                          26.0 ( 34)        17.2 ( 183)                       
                                       <NA>                                                                          0.0 (  0)         0.3 (   3)                       
  EP_composite % (freq)                No data available.                                                            0.0 (  0)         0.0 (   0)     NaN         0.8   
                                       No composite endpoints                                                       67.2 ( 88)        74.3 ( 791)                       
                                       Composite endpoints                                                          32.8 ( 43)        24.9 ( 265)                       
                                       <NA>                                                                          0.0 (  0)         0.8 (   9)                       
  EP_composite_time (mean (SD))                                                                                    2.614 (0.931)     2.613 (1.095)    0.992       0.9   
  macmean0 (mean (SD))                                                                                             0.837 (1.088)     0.781 (1.231)    0.623       2.3   
  smcmean0 (mean (SD))                                                                                             2.152 (1.861)     1.904 (2.222)    0.223       2.7   
  Macrophages.bin % (freq)             no/minor                                                                     48.9 ( 64)        47.4 ( 505)     0.583       1.9   
                                       moderate/heavy                                                               50.4 ( 66)        50.5 ( 538)                       
                                       <NA>                                                                          0.8 (  1)         2.1 (  22)                       
  SMC.bin % (freq)                     no/minor                                                                     22.9 ( 30)        32.2 ( 343)     0.085       1.8   
                                       moderate/heavy                                                               75.6 ( 99)        65.9 ( 702)                       
                                       <NA>                                                                          1.5 (  2)         1.9 (  20)                       
  neutrophils (mean (SD))                                                                                        157.643 (507.380) 172.872 (477.038)  0.876      81.9   
  Mast_cells_plaque (mean (SD))                                                                                  111.400 (112.037) 183.284 (180.156)  0.056      86.1   
  IPH.bin % (freq)                     no                                                                           41.2 ( 54)        37.9 ( 404)     0.561       1.7   
                                       yes                                                                          58.0 ( 76)        60.3 ( 642)                       
                                       <NA>                                                                          0.8 (  1)         1.8 (  19)                       
  vessel_density_averaged (mean (SD))                                                                              8.608 (6.547)     8.408 (6.469)    0.750       8.7   
 [ reached getOption("max.print") -- omitted 21 rows ]

CEA patients with MCP1_pg_ug_2015 and MCP1

Showing the baseline table of the CEA patients in the Athero-Express Biobank with MCP1_pg_ug_2015 and MCP1.


AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) | !is.na(MCP1))

AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.combo, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
                                      Stratified by AsymptSympt2G
                                       level                                                                     Asymptomatic      Symptomatic       p      test Missing
  n                                                                                                                  161              1165                              
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     52.2 ( 84)        47.0 ( 547)     0.246       0.0   
                                       UMC Utrecht                                                                  47.8 ( 77)        53.0 ( 618)                       
  ORyear % (freq)                      No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       2002                                                                         10.6 ( 17)         4.8 (  56)                       
                                       2003                                                                         11.8 ( 19)        10.6 ( 124)                       
                                       2004                                                                         19.9 ( 32)        12.2 ( 142)                       
                                       2005                                                                         13.7 ( 22)        13.3 ( 155)                       
                                       2006                                                                          8.7 ( 14)        10.0 ( 116)                       
                                       2007                                                                          9.3 ( 15)         9.6 ( 112)                       
                                       2008                                                                          6.2 ( 10)         6.8 (  79)                       
                                       2009                                                                          6.2 ( 10)         7.6 (  89)                       
                                       2010                                                                          4.3 (  7)         7.0 (  81)                       
                                       2011                                                                          5.0 (  8)         8.7 ( 101)                       
                                       2012                                                                          4.3 (  7)         7.6 (  88)                       
                                       2013                                                                          0.0 (  0)         1.8 (  21)                       
                                       2014                                                                          0.0 (  0)         0.1 (   1)                       
                                       2015                                                                          0.0 (  0)         0.0 (   0)                       
                                       2016                                                                          0.0 (  0)         0.0 (   0)                       
                                       2017                                                                          0.0 (  0)         0.0 (   0)                       
                                       2018                                                                          0.0 (  0)         0.0 (   0)                       
                                       2019                                                                          0.0 (  0)         0.0 (   0)                       
  Age (mean (SD))                                                                                                 65.901 (9.051)    68.788 (9.081)   <0.001       0.0   
  Gender % (freq)                      female                                                                       23.0 ( 37)        30.4 ( 354)     0.066       0.0   
                                       male                                                                         77.0 (124)        69.6 ( 811)                       
  TC_finalCU (mean (SD))                                                                                         179.199 (45.274)  183.983 (48.290)   0.331      32.7   
  LDL_finalCU (mean (SD))                                                                                        104.132 (37.590)  109.642 (41.227)   0.215      39.8   
  HDL_finalCU (mean (SD))                                                                                         44.749 (14.890)   45.808 (18.231)   0.568      36.1   
  TG_finalCU (mean (SD))                                                                                         158.699 (87.584)  145.942 (83.223)   0.143      35.6   
  TC_final (mean (SD))                                                                                             4.641 (1.173)     4.765 (1.251)    0.331      32.7   
  LDL_final (mean (SD))                                                                                            2.697 (0.974)     2.840 (1.068)    0.215      39.8   
  HDL_final (mean (SD))                                                                                            1.159 (0.386)     1.186 (0.472)    0.568      36.1   
  TG_final (mean (SD))                                                                                             1.793 (0.990)     1.649 (0.940)    0.143      35.6   
  hsCRP_plasma (mean (SD))                                                                                         6.846 (21.838)   16.213 (110.899)  0.393      40.6   
  systolic (mean (SD))                                                                                           152.838 (24.600)  155.742 (26.411)   0.225      13.5   
  diastoli (mean (SD))                                                                                            80.824 (12.855)   82.873 (13.549)   0.096      13.5   
  GFR_MDRD (mean (SD))                                                                                            70.440 (19.793)   71.901 (20.142)   0.396       3.5   
  BMI (mean (SD))                                                                                                 26.626 (3.572)    26.350 (3.768)    0.389       4.4   
  KDOQI % (freq)                       No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         3.5   
                                       Normal kidney function                                                       14.9 ( 24)        17.4 ( 203)                       
                                       CKD 2 (Mild)                                                                 50.9 ( 82)        53.4 ( 622)                       
                                       CKD 3 (Moderate)                                                             29.8 ( 48)        23.9 ( 279)                       
                                       CKD 4 (Severe)                                                                0.0 (  0)         1.3 (  15)                       
                                       CKD 5 (Failure)                                                               0.6 (  1)         0.4 (   5)                       
                                       <NA>                                                                          3.7 (  6)         3.5 (  41)                       
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         4.6   
                                       Underweight                                                                   1.2 (  2)         0.9 (  11)                       
                                       Normal                                                                       32.3 ( 52)        35.5 ( 414)                       
                                       Overweight                                                                   49.7 ( 80)        45.6 ( 531)                       
                                       Obese                                                                        13.7 ( 22)        13.1 ( 153)                       
                                       <NA>                                                                          3.1 (  5)         4.8 (  56)                       
  SmokerStatus % (freq)                Current smoker                                                               29.2 ( 47)        36.0 ( 419)     0.072       4.0   
                                       Ex-smoker                                                                    56.5 ( 91)        45.7 ( 532)                       
                                       Never smoked                                                                 11.8 ( 19)        14.2 ( 165)                       
                                       <NA>                                                                          2.5 (  4)         4.2 (  49)                       
  AlcoholUse % (freq)                  No                                                                           38.5 ( 62)        33.6 ( 392)     0.223       3.8   
                                       Yes                                                                          59.6 ( 96)        62.2 ( 725)                       
                                       <NA>                                                                          1.9 (  3)         4.1 (  48)                       
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 78.3 (126)        77.3 ( 900)     0.852       0.0   
                                       Diabetes                                                                     21.7 ( 35)        22.7 ( 265)                       
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         1.9   
                                       no                                                                           25.5 ( 41)        26.5 ( 309)                       
                                       yes                                                                          73.9 (119)        71.4 ( 832)                       
                                       <NA>                                                                          0.6 (  1)         2.1 (  24)                       
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         2.4   
                                       no                                                                           32.3 ( 52)        32.9 ( 383)                       
                                       yes                                                                          66.5 (107)        64.5 ( 752)                       
                                       <NA>                                                                          1.2 (  2)         2.6 (  30)                       
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       no                                                                           11.2 ( 18)        14.1 ( 164)                       
                                       yes                                                                          88.8 (143)        85.9 (1001)                       
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.2   
                                       no                                                                           15.5 ( 25)        22.7 ( 265)                       
                                       yes                                                                          83.9 (135)        77.1 ( 898)                       
                                       <NA>                                                                          0.6 (  1)         0.2 (   2)                       
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.2   
                                       no                                                                           89.4 (144)        88.0 (1025)                       
                                       yes                                                                           9.9 ( 16)        11.8 ( 138)                       
                                       <NA>                                                                          0.6 (  1)         0.2 (   2)                       
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.5   
                                       no                                                                            6.2 ( 10)        10.8 ( 126)                       
                                       yes                                                                          92.5 (149)        88.8 (1035)                       
                                       <NA>                                                                          1.2 (  2)         0.3 (   4)                       
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.2   
                                       no                                                                           17.4 ( 28)        23.2 ( 270)                       
                                       yes                                                                          82.0 (132)        76.7 ( 893)                       
                                       <NA>                                                                          0.6 (  1)         0.2 (   2)                       
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (  0)         0.0 (   0)     NaN         5.5   
                                       No stroke diagnosed                                                          80.1 (129)        75.5 ( 880)                       
                                       Stroke diagnosed                                                             13.7 ( 22)        19.1 ( 222)                       
                                       <NA>                                                                          6.2 ( 10)         5.4 (  63)                       
  sympt % (freq)                       missing                                                                       0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       Asymptomatic                                                                100.0 (161)         0.0 (   0)                       
                                       TIA                                                                           0.0 (  0)        46.6 ( 543)                       
                                       minor stroke                                                                  0.0 (  0)        17.2 ( 200)                       
                                       Major stroke                                                                  0.0 (  0)        11.6 ( 135)                       
                                       Amaurosis fugax                                                               0.0 (  0)        16.9 ( 197)                       
                                       Four vessel disease                                                           0.0 (  0)         2.1 (  25)                       
                                       Vertebrobasilary TIA                                                          0.0 (  0)         0.2 (   2)                       
                                       Retinal infarction                                                            0.0 (  0)         1.4 (  16)                       
                                       Symptomatic, but aspecific symtoms                                            0.0 (  0)         3.1 (  36)                       
                                       Contralateral symptomatic occlusion                                           0.0 (  0)         0.5 (   6)                       
                                       retinal infarction                                                            0.0 (  0)         0.3 (   3)                       
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (  0)         0.1 (   1)                       
                                       retinal infarction + TIAs                                                     0.0 (  0)         0.0 (   0)                       
                                       Ocular ischemic syndrome                                                      0.0 (  0)         0.1 (   1)                       
                                       ischemisch glaucoom                                                           0.0 (  0)         0.0 (   0)                       
                                       subclavian steal syndrome                                                     0.0 (  0)         0.0 (   0)                       
                                       TGA                                                                           0.0 (  0)         0.0 (   0)                       
  Symptoms.5G % (freq)                 Asymptomatic                                                                100.0 (161)         0.0 (   0)    <0.001       0.0   
                                       Ocular                                                                        0.0 (  0)        17.0 ( 198)                       
                                       Other                                                                         0.0 (  0)         5.8 (  68)                       
                                       Retinal infarction                                                            0.0 (  0)         1.6 (  19)                       
                                       Stroke                                                                        0.0 (  0)        28.8 ( 335)                       
                                       TIA                                                                           0.0 (  0)        46.8 ( 545)                       
  AsymptSympt % (freq)                 Asymptomatic                                                                100.0 (161)         0.0 (   0)    <0.001       0.0   
                                       Ocular and others                                                             0.0 (  0)        24.5 ( 285)                       
                                       Symptomatic                                                                   0.0 (  0)        75.5 ( 880)                       
  AsymptSympt2G % (freq)               Asymptomatic                                                                100.0 (161)         0.0 (   0)    <0.001       0.0   
                                       Symptomatic                                                                   0.0 (  0)       100.0 (1165)                       
  restenos % (freq)                    missing                                                                       0.0 (  0)         0.0 (   0)     NaN         2.0   
                                       de novo                                                                      93.2 (150)        95.0 (1107)                       
                                       restenosis                                                                    3.7 (  6)         3.1 (  36)                       
                                       stenose bij angioseal na PTCA                                                 0.0 (  0)         0.0 (   0)                       
                                       <NA>                                                                          3.1 (  5)         1.9 (  22)                       
  stenose % (freq)                     missing                                                                       0.0 (  0)         0.0 (   0)     NaN         2.9   
                                       0-49%                                                                         0.0 (  0)         0.6 (   7)                       
                                       50-70%                                                                        2.5 (  4)         6.2 (  72)                       
                                       70-90%                                                                       50.9 ( 82)        44.6 ( 520)                       
                                       90-99%                                                                       42.9 ( 69)        43.3 ( 504)                       
                                       100% (Occlusion)                                                              0.0 (  0)         0.9 (  11)                       
                                       NA                                                                            0.0 (  0)         0.0 (   0)                       
                                       50-99%                                                                        0.6 (  1)         0.3 (   4)                       
                                       70-99%                                                                        0.0 (  0)         1.2 (  14)                       
                                       99                                                                            0.0 (  0)         0.0 (   0)                       
                                       <NA>                                                                          3.1 (  5)         2.8 (  33)                       
  MedHx_CVD % (freq)                   No                                                                           37.3 ( 60)        36.7 ( 428)     0.965       0.0   
                                       yes                                                                          62.7 (101)        63.3 ( 737)                       
  CAD_history % (freq)                 Missing                                                                       0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       No history CAD                                                               59.0 ( 95)        69.1 ( 805)                       
                                       History CAD                                                                  41.0 ( 66)        30.9 ( 360)                       
  PAOD % (freq)                        missing/no data                                                               0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       no                                                                           73.9 (119)        79.8 ( 930)                       
                                       yes                                                                          26.1 ( 42)        20.2 ( 235)                       
  Peripheral.interv % (freq)           no                                                                           72.7 (117)        83.0 ( 967)     0.004       0.2   
                                       yes                                                                          27.3 ( 44)        16.7 ( 195)                       
                                       <NA>                                                                          0.0 (  0)         0.3 (   3)                       
  EP_composite % (freq)                No data available.                                                            0.0 (  0)         0.0 (   0)     NaN         0.8   
                                       No composite endpoints                                                       68.3 (110)        73.8 ( 860)                       
                                       Composite endpoints                                                          31.7 ( 51)        25.2 ( 294)                       
                                       <NA>                                                                          0.0 (  0)         0.9 (  11)                       
  EP_composite_time (mean (SD))                                                                                    2.579 (0.961)     2.611 (1.130)    0.735       1.0   
  macmean0 (mean (SD))                                                                                             0.802 (1.072)     0.822 (1.275)    0.856       2.2   
  smcmean0 (mean (SD))                                                                                             2.445 (2.594)     1.923 (2.234)    0.007       2.5   
  Macrophages.bin % (freq)             no/minor                                                                     50.3 ( 81)        45.8 ( 533)     0.309       1.8   
                                       moderate/heavy                                                               49.1 ( 79)        52.3 ( 609)                       
                                       <NA>                                                                          0.6 (  1)         2.0 (  23)                       
  SMC.bin % (freq)                     no/minor                                                                     21.7 ( 35)        32.5 ( 379)     0.017       1.7   
                                       moderate/heavy                                                               77.0 (124)        65.8 ( 766)                       
                                       <NA>                                                                          1.2 (  2)         1.7 (  20)                       
  neutrophils (mean (SD))                                                                                        133.447 (437.032) 158.140 (448.512)  0.754      80.9   
  Mast_cells_plaque (mean (SD))                                                                                  123.389 (135.924) 173.244 (168.601)  0.097      83.7   
  IPH.bin % (freq)                     no                                                                           39.1 ( 63)        36.3 ( 423)     0.512       1.5   
                                       yes                                                                          60.2 ( 97)        62.1 ( 723)                       
                                       <NA>                                                                          0.6 (  1)         1.6 (  19)                       
  vessel_density_averaged (mean (SD))                                                                              8.837 (6.727)     8.439 (6.394)    0.480       8.0   
 [ reached getOption("max.print") -- omitted 21 rows ]

CEA patients with plasma MCP1 levels

Showing the baseline table of the CEA patients in the Athero-Express Biobank with plasma MCP1 levels.

NOT AVAILABLE YET

AEDB.CEA.subset.serum <- subset(AEDB.CEA, !is.na(MCP1_plasma))

AEDB.CEA.subset.serum.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.serum, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]

CEA patients with plasma and plaque MCP1 levels

Showing the baseline table of the CEA patients in the Athero-Express Biobank with both plasma and plaque MCP1 levels.

NOT AVAILABLE YET

AEDB.CEA.subset.both <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) & !is.na(MCP1))

AEDB.CEA.subset.both.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.both, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]

Writing the baseline table to Excel format.

# Write basetable
require(openxlsx)
Loading required package: openxlsx
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
           AEDB.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
           AEDB.CEA.subset.AsymptSympt.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline_Sympt")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
           AEDB.CEA.subset.combo.tableOne,
           row.names = TRUE,
           col.names = TRUE,
           sheetName = "subsetAEDB_Baseline")

# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAserum.AsymptSympt.xlsx"),
#            AEDB.CEA.subset.serum.tableOne, 
#            row.names = TRUE, 
#            col.names = TRUE, 
#            sheetName = "subsetAEDB_Baseline_serum_Sympt")

Data exploration

Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation.

MCP1 plaque levels

We will explore the plaque levels. As noted above, we will use MCP1_pg_ug_2015.


summary(AEDB.CEA$MCP1_pg_ug_2015)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0005  0.1373  0.3401  0.6121  0.7232 10.8540    1225 
do.call(rbind , by(AEDB.CEA$MCP1_pg_ug_2015, AEDB.CEA$AsymptSympt2G, summary))
                     Min.    1st Qu.    Median      Mean   3rd Qu.      Max. NA's
Asymptomatic 0.0061401090 0.09779678 0.2148984 0.4950529 0.4982360  5.761795  139
Symptomatic  0.0004584575 0.14408036 0.3510514 0.6264834 0.7412862 10.853968 1086
library(patchwork)

Attaching package: ‘patchwork’

The following object is masked from ‘package:MASS’:

    area
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.
min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015
[1] 0.0004584575
AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    # title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.
p1 

p2 

p3

# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)

MCP1 serum levels

NOT AVAILABLE YET


summary(AEDB.CEA$MCP1)

do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
do.call(rbind , by(AEDB.CEA.subset.serum$MCP1_pg_ug_2015, AEDB.CEA.subset.serum$AsymptSympt2G, summary))
do.call(rbind , by(AEDB.CEA.subset.serum$MCP1, AEDB.CEA.subset.serum$AsymptSympt2G, summary))
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())

min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1

AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 serum levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)

Preliminary conclusion data exploration

In line with the previous work by Marios Georgakis we will apply natural log transformation on all proteins and focus the analysis on MCP1 in serum and plaque.

Analyses

The analyses are focused on three elements:

  1. plaque vulnerability phenotypes
  2. clinical status at inclusion (symptoms)
  3. secondary clinical outcome during three (3) years of follow-up

Covariates & other variables

  1. Age (continuous in 1-year increment). [Age]
  2. Sex (male vs. female). [Gender]
  3. Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
  4. Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [DiabetesStatus]
  5. Smoking (current, ex-, never). [SmokerStatus]
  6. LDL-C levels (continuous). [LDL_final]
  7. Use of lipid-lowering drugs. [Med.Statin.LLD]
  8. Use of antiplatelet drugs. [Med.all.antiplatelet]
  9. eGFR (continuous). [GFR_MDRD]
  10. BMI (continuous). [BMI]
  11. History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combination of [CAD_history, Stroke_history, Peripheral.interv]
  12. Level of stenosis (50-70% vs. 70-99%). [stenose]

Models

We will analyze the data through four different models

  • Model 1: adjusted for age and sex
  • Model 2: adjusted for age, sex, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque and serum MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:

  • Percentage of macrophages (continuous trait)
  • Percentage of SMCs (continuous trait)
  • Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
  • Presence of moderate/heavy calcifications (binary trait)
  • Presence of moderate/heavy collagen content (binary trait)
  • Presence of lipid core no/<10% vs. >10% (binary trait)
  • Presence of intraplaque hemorrhage (binary trait)

Continous traits


# macrophages
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$macmean0)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0733  0.3133  0.7676  1.0000 15.1000     720 
min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

Minimum value % macrophages: 0.
AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)

ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

# smooth muscle cells
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$macmean0)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0733  0.3133  0.7676  1.0000 15.1000     720 
min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

Minimum value % smooth muscle cells: 0.
AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)

ggpubr::gghistogram(AEDB.CEA, "SMC_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

# vessel density
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$vessel_density_averaged)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   4.000   7.000   8.318  11.300  48.000     850 
min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity
[1] 0
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

Minimum value number of intraplaque neovessels per 3-4 hotspots: 0.
AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)

ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                    xlab = "natural log-transformed number", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

Binary traits


# calcification
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$Calc.bin)
      no/minor moderate/heavy           NA's 
          1006            849            566 
contrasts(AEDB.CEA$Calc.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CalcificationPlaque)) %>%
  group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())

rm(df)

# collagen
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$Collagen.bin)
      no/minor moderate/heavy           NA's 
           382           1467            572 
contrasts(AEDB.CEA$Collagen.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CollagenPlaque)) %>%
  group_by(Gender, CollagenPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())

rm(df)

# fat 10%
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$Fat.bin_10)
 <10%  >10%  NA's 
  542  1314   565 
contrasts(AEDB.CEA$Fat.bin_10)
       >10%
 <10%     0
 >10%     1
AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)

df <- AEDB.CEA %>%
  filter(!is.na(Fat10Perc)) %>%
  group_by(Gender, Fat10Perc) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())

rm(df)

# IPH
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$IPH.bin)
  no  yes NA's 
 744 1108  569 
contrasts(AEDB.CEA$IPH.bin)
    yes
no    0
yes   1
AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)

df <- AEDB.CEA %>%
  filter(!is.na(IPH)) %>%
  group_by(Gender, IPH) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())

rm(df)

# Symptoms
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$AsymptSympt)
     Asymptomatic Ocular and others       Symptomatic 
              270               540              1611 
contrasts(AEDB.CEA$AsymptSympt)
                  Ocular and others Symptomatic
Asymptomatic                      0           0
Ocular and others                 1           0
Symptomatic                       0           1
AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)

df <- AEDB.CEA %>%
  filter(!is.na(AsymptSympt)) %>%
  group_by(Gender, AsymptSympt) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())

rm(df)

In this section we make some variables to assist with analysis.

AEDB.CEA.samplesize = nrow(AEDB.CEA)
# TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
# TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")
# TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_rank")
TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank")

# TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN") 
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.   BMI (continuous). [BMI]
# 11.   History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.   Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

COVARIATES_M1 = c("Age", "Gender")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")

# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

# COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
# COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")

Model 1

In this model we correct for Age and Gender.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of MCP1_pg_ug_2015_rank.

- processing Macrophages_rank
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(PROTEIN)` instead of `PROTEIN` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(TRAIT)` instead of `TRAIT` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(COVARIATES_M1)` instead of `COVARIATES_M1` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale  
          -0.08694            -0.04450             0.10580  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4082 -0.6842  0.0046  0.6589  3.3363 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -0.0417401  0.2249607  -0.186   0.8528  
currentDF[, TRAIT] -0.0450829  0.0294144  -1.533   0.1256  
Age                -0.0006612  0.0031991  -0.207   0.8363  
Gendermale          0.1060371  0.0635635   1.668   0.0955 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9995 on 1164 degrees of freedom
Multiple R-squared:  0.004088,  Adjusted R-squared:  0.001522 
F-statistic: 1.593 on 3 and 1164 DF,  p-value: 0.1894

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: -0.045083 
Standard error............: 0.029414 
Odds ratio (effect size)..: 0.956 
Lower 95% CI..............: 0.902 
Upper 95% CI..............: 1.013 
T-value...................: -1.532681 
P-value...................: 0.1256263 
R^2.......................: 0.004088 
Adjusted r^2..............: 0.001522 
Sample size of AE DB......: 2421 
Sample size of model......: 1168 
Missing data %............: 51.75547 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
          -0.01596            -0.11159  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4354 -0.6542 -0.0098  0.6350  3.3670 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.112040   0.228792   0.490 0.624438    
currentDF[, TRAIT] -0.111735   0.030680  -3.642 0.000283 ***
Age                -0.002497   0.003236  -0.772 0.440423    
Gendermale          0.062484   0.063873   0.978 0.328152    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9952 on 1160 degrees of freedom
Multiple R-squared:  0.01319,   Adjusted R-squared:  0.01064 
F-statistic: 5.169 on 3 and 1160 DF,  p-value: 0.001498

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.111735 
Standard error............: 0.03068 
Odds ratio (effect size)..: 0.894 
Lower 95% CI..............: 0.842 
Upper 95% CI..............: 0.95 
T-value...................: -3.641935 
P-value...................: 0.000282537 
R^2.......................: 0.013191 
Adjusted r^2..............: 0.010639 
Sample size of AE DB......: 2421 
Sample size of model......: 1164 
Missing data %............: 51.92069 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale  
           -0.1146             -0.1291              0.1266  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2793 -0.6743  0.0015  0.6370  3.4081 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -0.142513   0.233373  -0.611   0.5415    
currentDF[, TRAIT] -0.129023   0.030547  -4.224  2.6e-05 ***
Age                 0.000407   0.003312   0.123   0.9022    
Gendermale          0.126560   0.065772   1.924   0.0546 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.002 on 1088 degrees of freedom
Multiple R-squared:  0.01916,   Adjusted R-squared:  0.01645 
F-statistic: 7.084 on 3 and 1088 DF,  p-value: 0.0001025

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.129023 
Standard error............: 0.030547 
Odds ratio (effect size)..: 0.879 
Lower 95% CI..............: 0.828 
Upper 95% CI..............: 0.933 
T-value...................: -4.223732 
P-value...................: 2.60385e-05 
R^2.......................: 0.019159 
Adjusted r^2..............: 0.016454 
Sample size of AE DB......: 2421 
Sample size of model......: 1092 
Missing data %............: 54.89467 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Binary plaque traits

Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of MCP1_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
            -0.88365              -0.44949               0.01414              -0.19877  

Degrees of Freedom: 1177 Total (i.e. Null);  1174 Residual
Null Deviance:      1632 
Residual Deviance: 1570     AIC: 1578

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7790  -1.1167  -0.7616   1.1294   1.9050  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.883647   0.460710  -1.918   0.0551 .  
currentDF[, PROTEIN] -0.449493   0.062887  -7.148 8.83e-13 ***
Age                   0.014139   0.006548   2.159   0.0308 *  
Gendermale           -0.198771   0.129909  -1.530   0.1260    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1632.5  on 1177  degrees of freedom
Residual deviance: 1570.1  on 1174  degrees of freedom
AIC: 1578.1

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.449493 
Standard error............: 0.062887 
Odds ratio (effect size)..: 0.638 
Lower 95% CI..............: 0.564 
Upper 95% CI..............: 0.722 
Z-value...................: -7.147681 
P-value...................: 8.825609e-13 
Hosmer and Lemeshow r^2...: 0.038193 
Cox and Snell r^2.........: 0.051552 
Nagelkerke's pseudo r^2...: 0.068747 
Sample size of AE DB......: 2421 
Sample size of model......: 1178 
Missing data %............: 51.34242 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN], 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]  
              1.3310               -0.2324  

Degrees of Freedom: 1178 Total (i.e. Null);  1177 Residual
Null Deviance:      1216 
Residual Deviance: 1205     AIC: 1209

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1020   0.5729   0.6568   0.7158   0.9553  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)           1.143689   0.546619   2.092  0.03641 * 
currentDF[, PROTEIN] -0.231697   0.072337  -3.203  0.00136 **
Age                   0.002892   0.007791   0.371  0.71047   
Gendermale           -0.015641   0.156179  -0.100  0.92023   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1215.6  on 1178  degrees of freedom
Residual deviance: 1205.0  on 1175  degrees of freedom
AIC: 1213

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.231697 
Standard error............: 0.072337 
Odds ratio (effect size)..: 0.793 
Lower 95% CI..............: 0.688 
Upper 95% CI..............: 0.914 
Z-value...................: -3.202999 
P-value...................: 0.001360044 
Hosmer and Lemeshow r^2...: 0.008726 
Cox and Snell r^2.........: 0.008956 
Nagelkerke's pseudo r^2...: 0.013921 
Sample size of AE DB......: 2421 
Sample size of model......: 1179 
Missing data %............: 51.30112 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
            -0.32933               0.15569               0.01093               0.85630  

Degrees of Freedom: 1178 Total (i.e. Null);  1175 Residual
Null Deviance:      1386 
Residual Deviance: 1339     AIC: 1347

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9399  -1.2794   0.6824   0.7695   1.2262  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.329327   0.501681  -0.656   0.5115    
currentDF[, PROTEIN]  0.155685   0.066807   2.330   0.0198 *  
Age                   0.010928   0.007185   1.521   0.1283    
Gendermale            0.856297   0.137117   6.245 4.24e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1386.5  on 1178  degrees of freedom
Residual deviance: 1338.8  on 1175  degrees of freedom
AIC: 1346.8

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.155685 
Standard error............: 0.066807 
Odds ratio (effect size)..: 1.168 
Lower 95% CI..............: 1.025 
Upper 95% CI..............: 1.332 
Z-value...................: 2.330372 
P-value...................: 0.01978647 
Hosmer and Lemeshow r^2...: 0.034376 
Cox and Snell r^2.........: 0.039619 
Nagelkerke's pseudo r^2...: 0.057296 
Sample size of AE DB......: 2421 
Sample size of model......: 1179 
Missing data %............: 51.30112 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale  
             0.02945              -0.12895               0.61756  

Degrees of Freedom: 1175 Total (i.e. Null);  1173 Residual
Null Deviance:      1572 
Residual Deviance: 1546     AIC: 1552

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.650  -1.271   0.884   0.956   1.342  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.157708   0.462022  -0.341   0.7328    
currentDF[, PROTEIN] -0.128693   0.060765  -2.118   0.0342 *  
Age                   0.002737   0.006577   0.416   0.6773    
Gendermale            0.616805   0.129037   4.780 1.75e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1572.3  on 1175  degrees of freedom
Residual deviance: 1545.6  on 1172  degrees of freedom
AIC: 1553.6

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: -0.128693 
Standard error............: 0.060765 
Odds ratio (effect size)..: 0.879 
Lower 95% CI..............: 0.781 
Upper 95% CI..............: 0.99 
Z-value...................: -2.117883 
P-value...................: 0.03418496 
Hosmer and Lemeshow r^2...: 0.016998 
Cox and Snell r^2.........: 0.022471 
Nagelkerke's pseudo r^2...: 0.030474 
Sample size of AE DB......: 2421 
Sample size of model......: 1176 
Missing data %............: 51.42503 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of MCP1_pg_ug_2015_rank.

- processing Macrophages_rank
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(COVARIATES_M2)` instead of `COVARIATES_M2` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    Med.Statin.LLD, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes          Med.Statin.LLDyes  
                  0.30069                   -0.05373                   -0.20206                   -0.14604  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3053 -0.6548 -0.0242  0.6293  3.3436 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                0.2390578  0.6042876   0.396   0.6925  
currentDF[, TRAIT]        -0.0612856  0.0317785  -1.929   0.0541 .
Age                       -0.0022303  0.0039334  -0.567   0.5708  
Gendermale                 0.0974448  0.0709000   1.374   0.1696  
Hypertension.compositeyes -0.2024463  0.0966092  -2.096   0.0364 *
DiabetesStatusDiabetes    -0.0289768  0.0772772  -0.375   0.7078  
SmokerStatusEx-smoker     -0.0230909  0.0731098  -0.316   0.7522  
SmokerStatusNever smoked   0.1337644  0.1030877   1.298   0.1947  
Med.Statin.LLDyes         -0.1502085  0.0774112  -1.940   0.0526 .
Med.all.antiplateletyes    0.0018191  0.1086828   0.017   0.9866  
GFR_MDRD                  -0.0005437  0.0016784  -0.324   0.7461  
BMI                       -0.0026857  0.0088250  -0.304   0.7609  
MedHx_CVDyes               0.0199269  0.0664034   0.300   0.7642  
stenose50-70%              0.2704089  0.4317030   0.626   0.5312  
stenose70-90%              0.3081981  0.4140028   0.744   0.4568  
stenose90-99%              0.1802418  0.4140082   0.435   0.6634  
stenose100% (Occlusion)   -0.2457719  0.5317403  -0.462   0.6440  
stenose50-99%              0.4778476  0.6484366   0.737   0.4613  
stenose70-99%              0.4432860  0.5811508   0.763   0.4458  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.002 on 1000 degrees of freedom
Multiple R-squared:  0.0227,    Adjusted R-squared:  0.005108 
F-statistic:  1.29 on 18 and 1000 DF,  p-value: 0.1851

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: -0.061286 
Standard error............: 0.031778 
Odds ratio (effect size)..: 0.941 
Lower 95% CI..............: 0.884 
Upper 95% CI..............: 1.001 
T-value...................: -1.928524 
P-value...................: 0.05407278 
R^2.......................: 0.0227 
Adjusted r^2..............: 0.005108 
Sample size of AE DB......: 2421 
Sample size of model......: 1019 
Missing data %............: 57.90995 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    Med.Statin.LLD, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes          Med.Statin.LLDyes  
                   0.3084                    -0.1064                    -0.2034                    -0.1530  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3130 -0.6745 -0.0099  0.6299  3.3696 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.3219731  0.6046171   0.533  0.59448   
currentDF[, TRAIT]        -0.1029968  0.0329730  -3.124  0.00184 **
Age                       -0.0034454  0.0039631  -0.869  0.38485   
Gendermale                 0.0487194  0.0717747   0.679  0.49743   
Hypertension.compositeyes -0.1954166  0.0965630  -2.024  0.04327 * 
DiabetesStatusDiabetes    -0.0219028  0.0772121  -0.284  0.77672   
SmokerStatusEx-smoker     -0.0209122  0.0731694  -0.286  0.77509   
SmokerStatusNever smoked   0.1054769  0.1030387   1.024  0.30624   
Med.Statin.LLDyes         -0.1609021  0.0774630  -2.077  0.03804 * 
Med.all.antiplateletyes   -0.0039324  0.1085928  -0.036  0.97112   
GFR_MDRD                  -0.0004018  0.0016792  -0.239  0.81092   
BMI                       -0.0026011  0.0088289  -0.295  0.76836   
MedHx_CVDyes               0.0116355  0.0664377   0.175  0.86101   
stenose50-70%              0.3063875  0.4313296   0.710  0.47766   
stenose70-90%              0.3397682  0.4136695   0.821  0.41164   
stenose90-99%              0.2223931  0.4138010   0.537  0.59108   
stenose100% (Occlusion)   -0.1771374  0.5313862  -0.333  0.73894   
stenose50-99%              0.5447894  0.6479149   0.841  0.40064   
stenose70-99%              0.4871557  0.5806444   0.839  0.40168   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.001 on 996 degrees of freedom
Multiple R-squared:  0.02833,   Adjusted R-squared:  0.01077 
F-statistic: 1.613 on 18 and 996 DF,  p-value: 0.05023

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.102997 
Standard error............: 0.032973 
Odds ratio (effect size)..: 0.902 
Lower 95% CI..............: 0.846 
Upper 95% CI..............: 0.962 
T-value...................: -3.123675 
P-value...................: 0.00183768 
R^2.......................: 0.028328 
Adjusted r^2..............: 0.010768 
Sample size of AE DB......: 2421 
Sample size of model......: 1015 
Missing data %............: 58.07518 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    Med.Statin.LLD, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes          Med.Statin.LLDyes  
                   0.2912                    -0.1439                    -0.1849                    -0.1662  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1614 -0.6484 -0.0233  0.6402  3.4332 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.1767459  0.6402229   0.276   0.7826    
currentDF[, TRAIT]        -0.1420732  0.0332558  -4.272 2.13e-05 ***
Age                       -0.0009175  0.0040589  -0.226   0.8212    
Gendermale                 0.1109606  0.0733445   1.513   0.1307    
Hypertension.compositeyes -0.1942298  0.1002766  -1.937   0.0531 .  
DiabetesStatusDiabetes    -0.0328877  0.0819413  -0.401   0.6882    
SmokerStatusEx-smoker     -0.0313111  0.0760495  -0.412   0.6806    
SmokerStatusNever smoked   0.1139310  0.1071394   1.063   0.2879    
Med.Statin.LLDyes         -0.1671740  0.0796915  -2.098   0.0362 *  
Med.all.antiplateletyes    0.0397565  0.1144791   0.347   0.7285    
GFR_MDRD                  -0.0010896  0.0017515  -0.622   0.5340    
BMI                       -0.0016953  0.0091572  -0.185   0.8532    
MedHx_CVDyes               0.0074801  0.0688297   0.109   0.9135    
stenose50-70%              0.1841594  0.4727540   0.390   0.6970    
stenose70-90%              0.2563949  0.4542951   0.564   0.5726    
stenose90-99%              0.1436685  0.4538378   0.317   0.7516    
stenose100% (Occlusion)   -0.2562619  0.5637405  -0.455   0.6495    
stenose50-99%              0.5911304  0.6754523   0.875   0.3817    
stenose70-99%              0.1673943  0.6766921   0.247   0.8047    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.005 on 932 degrees of freedom
Multiple R-squared:  0.03977,   Adjusted R-squared:  0.02122 
F-statistic: 2.144 on 18 and 932 DF,  p-value: 0.003707

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.142073 
Standard error............: 0.033256 
Odds ratio (effect size)..: 0.868 
Lower 95% CI..............: 0.813 
Upper 95% CI..............: 0.926 
T-value...................: -4.272134 
P-value...................: 2.134847e-05 
R^2.......................: 0.039768 
Adjusted r^2..............: 0.021223 
Sample size of AE DB......: 2421 
Sample size of model......: 951 
Missing data %............: 60.71871 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Binary plaque traits

Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of MCP1_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + SmokerStatus + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)      currentDF[, PROTEIN]                       Age     SmokerStatusEx-smoker  SmokerStatusNever smoked             stenose50-70%             stenose70-90%  
                -1.16173                  -0.47346                   0.01896                  -0.40609                  -0.44306                  -0.56758                  -0.01349  
           stenose90-99%   stenose100% (Occlusion)             stenose50-99%             stenose70-99%  
                 0.18538                   1.15456                 -14.43672                  -0.79787  

Degrees of Freedom: 1023 Total (i.e. Null);  1013 Residual
Null Deviance:      1417 
Residual Deviance: 1335     AIC: 1357

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8447  -1.0810  -0.7025   1.1078   2.0019  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -1.522e+00  1.248e+00  -1.220  0.22262    
currentDF[, PROTEIN]      -4.740e-01  6.875e-02  -6.895 5.38e-12 ***
Age                        1.797e-02  8.224e-03   2.185  0.02892 *  
Gendermale                -9.953e-02  1.469e-01  -0.677  0.49809    
Hypertension.compositeyes  2.389e-01  2.017e-01   1.185  0.23618    
DiabetesStatusDiabetes    -2.266e-01  1.619e-01  -1.400  0.16160    
SmokerStatusEx-smoker     -4.096e-01  1.525e-01  -2.687  0.00722 ** 
SmokerStatusNever smoked  -4.851e-01  2.157e-01  -2.249  0.02454 *  
Med.Statin.LLDyes         -1.636e-01  1.607e-01  -1.019  0.30842    
Med.all.antiplateletyes   -7.738e-02  2.248e-01  -0.344  0.73071    
GFR_MDRD                  -5.302e-04  3.511e-03  -0.151  0.87997    
BMI                        2.166e-02  1.839e-02   1.178  0.23892    
MedHx_CVDyes              -1.660e-02  1.377e-01  -0.121  0.90406    
stenose50-70%             -5.216e-01  8.838e-01  -0.590  0.55510    
stenose70-90%              9.803e-03  8.419e-01   0.012  0.99071    
stenose90-99%              2.028e-01  8.420e-01   0.241  0.80969    
stenose100% (Occlusion)    1.181e+00  1.175e+00   1.005  0.31496    
stenose50-99%             -1.444e+01  4.192e+02  -0.034  0.97252    
stenose70-99%             -7.533e-01  1.208e+00  -0.624  0.53294    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1417.5  on 1023  degrees of freedom
Residual deviance: 1329.0  on 1005  degrees of freedom
AIC: 1367

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.474031 
Standard error............: 0.068749 
Odds ratio (effect size)..: 0.622 
Lower 95% CI..............: 0.544 
Upper 95% CI..............: 0.712 
Z-value...................: -6.895134 
P-value...................: 5.381419e-12 
Hosmer and Lemeshow r^2...: 0.062418 
Cox and Snell r^2.........: 0.082776 
Nagelkerke's pseudo r^2...: 0.110443 
Sample size of AE DB......: 2421 
Sample size of model......: 1024 
Missing data %............: 57.70343 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerStatus + BMI + MedHx_CVD, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)      currentDF[, PROTEIN]     SmokerStatusEx-smoker  SmokerStatusNever smoked                       BMI              MedHx_CVDyes  
                 0.47638                  -0.24086                  -0.38304                  -0.63224                   0.03905                   0.25051  

Degrees of Freedom: 1024 Total (i.e. Null);  1019 Residual
Null Deviance:      1048 
Residual Deviance: 1025     AIC: 1037

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2698   0.4534   0.6199   0.7192   1.1840  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.385e+01  9.628e+02   0.014  0.98852   
currentDF[, PROTEIN]      -2.330e-01  7.922e-02  -2.941  0.00327 **
Age                        1.362e-02  9.731e-03   1.400  0.16155   
Gendermale                -4.542e-02  1.763e-01  -0.258  0.79672   
Hypertension.compositeyes  2.446e-01  2.286e-01   1.070  0.28467   
DiabetesStatusDiabetes     7.902e-02  1.972e-01   0.401  0.68861   
SmokerStatusEx-smoker     -4.387e-01  1.893e-01  -2.317  0.02051 * 
SmokerStatusNever smoked  -7.570e-01  2.485e-01  -3.046  0.00232 **
Med.Statin.LLDyes          2.795e-03  1.921e-01   0.015  0.98839   
Med.all.antiplateletyes    2.526e-01  2.597e-01   0.973  0.33067   
GFR_MDRD                   5.011e-03  4.226e-03   1.186  0.23576   
BMI                        4.166e-02  2.314e-02   1.801  0.07175 . 
MedHx_CVDyes               2.219e-01  1.635e-01   1.357  0.17468   
stenose50-70%             -1.480e+01  9.628e+02  -0.015  0.98774   
stenose70-90%             -1.509e+01  9.628e+02  -0.016  0.98750   
stenose90-99%             -1.519e+01  9.628e+02  -0.016  0.98741   
stenose100% (Occlusion)    1.169e-01  1.242e+03   0.000  0.99992   
stenose50-99%             -3.410e-02  1.515e+03   0.000  0.99998   
stenose70-99%             -1.464e+01  9.628e+02  -0.015  0.98787   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1047.6  on 1024  degrees of freedom
Residual deviance: 1010.2  on 1006  degrees of freedom
AIC: 1048.2

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.232976 
Standard error............: 0.079216 
Odds ratio (effect size)..: 0.792 
Lower 95% CI..............: 0.678 
Upper 95% CI..............: 0.925 
Z-value...................: -2.941031 
P-value...................: 0.003271218 
Hosmer and Lemeshow r^2...: 0.035759 
Cox and Snell r^2.........: 0.035889 
Nagelkerke's pseudo r^2...: 0.056063 
Sample size of AE DB......: 2421 
Sample size of model......: 1025 
Missing data %............: 57.66212 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + SmokerStatus + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)      currentDF[, PROTEIN]                Gendermale     SmokerStatusEx-smoker  SmokerStatusNever smoked             stenose50-70%             stenose70-90%  
                 13.8072                    0.1608                    0.9217                   -0.2543                    0.3188                  -13.4987                  -13.3997  
           stenose90-99%   stenose100% (Occlusion)             stenose50-99%             stenose70-99%  
                -13.2648                  -13.8075                  -15.8024                  -14.6344  

Degrees of Freedom: 1024 Total (i.e. Null);  1014 Residual
Null Deviance:      1208 
Residual Deviance: 1153     AIC: 1175

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0874  -1.2279   0.6773   0.7958   1.5145  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.323e+01  3.540e+02   0.037   0.9702    
currentDF[, PROTEIN]       1.538e-01  7.309e-02   2.105   0.0353 *  
Age                        7.892e-03  8.954e-03   0.881   0.3781    
Gendermale                 9.325e-01  1.563e-01   5.966 2.43e-09 ***
Hypertension.compositeyes -3.351e-02  2.222e-01  -0.151   0.8802    
DiabetesStatusDiabetes    -1.599e-01  1.759e-01  -0.909   0.3634    
SmokerStatusEx-smoker     -2.887e-01  1.683e-01  -1.716   0.0862 .  
SmokerStatusNever smoked   2.644e-01  2.474e-01   1.069   0.2852    
Med.Statin.LLDyes         -2.292e-01  1.845e-01  -1.242   0.2141    
Med.all.antiplateletyes    1.095e-01  2.495e-01   0.439   0.6608    
GFR_MDRD                   4.634e-04  3.879e-03   0.119   0.9049    
BMI                        4.721e-03  1.983e-02   0.238   0.8118    
MedHx_CVDyes               7.604e-02  1.518e-01   0.501   0.6164    
stenose50-70%             -1.352e+01  3.539e+02  -0.038   0.9695    
stenose70-90%             -1.339e+01  3.539e+02  -0.038   0.9698    
stenose90-99%             -1.327e+01  3.539e+02  -0.037   0.9701    
stenose100% (Occlusion)   -1.382e+01  3.540e+02  -0.039   0.9689    
stenose50-99%             -1.581e+01  3.540e+02  -0.045   0.9644    
stenose70-99%             -1.472e+01  3.540e+02  -0.042   0.9668    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1207.9  on 1024  degrees of freedom
Residual deviance: 1149.4  on 1006  degrees of freedom
AIC: 1187.4

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.153844 
Standard error............: 0.073089 
Odds ratio (effect size)..: 1.166 
Lower 95% CI..............: 1.011 
Upper 95% CI..............: 1.346 
Z-value...................: 2.104886 
P-value...................: 0.03530121 
Hosmer and Lemeshow r^2...: 0.048471 
Cox and Snell r^2.........: 0.055521 
Nagelkerke's pseudo r^2...: 0.080203 
Sample size of AE DB......: 2421 
Sample size of model......: 1025 
Missing data %............: 57.66212 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Med.Statin.LLD + BMI + MedHx_CVD, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale     Med.Statin.LLDyes                   BMI          MedHx_CVDyes  
            -0.71077              -0.14275               0.51657              -0.27539               0.03056               0.36067  

Degrees of Freedom: 1022 Total (i.e. Null);  1017 Residual
Null Deviance:      1367 
Residual Deviance: 1337     AIC: 1349

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9526  -1.2816   0.8168   0.9931   1.5440  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -0.3744407  1.2806515  -0.292   0.7700    
currentDF[, PROTEIN]      -0.1420106  0.0661286  -2.147   0.0318 *  
Age                        0.0002745  0.0081696   0.034   0.9732    
Gendermale                 0.5755452  0.1456998   3.950 7.81e-05 ***
Hypertension.compositeyes -0.1304980  0.2014410  -0.648   0.5171    
DiabetesStatusDiabetes    -0.1239874  0.1610422  -0.770   0.4414    
SmokerStatusEx-smoker     -0.1106836  0.1537985  -0.720   0.4717    
SmokerStatusNever smoked  -0.1474556  0.2125316  -0.694   0.4878    
Med.Statin.LLDyes         -0.2598492  0.1641604  -1.583   0.1134    
Med.all.antiplateletyes    0.0938507  0.2263983   0.415   0.6785    
GFR_MDRD                  -0.0048629  0.0035299  -1.378   0.1683    
BMI                        0.0346557  0.0186016   1.863   0.0625 .  
MedHx_CVDyes               0.3433350  0.1371721   2.503   0.0123 *  
stenose50-70%             -0.3005803  0.9270679  -0.324   0.7458    
stenose70-90%             -0.1234783  0.8929528  -0.138   0.8900    
stenose90-99%              0.1233878  0.8933095   0.138   0.8901    
stenose100% (Occlusion)   -0.3672092  1.1204001  -0.328   0.7431    
stenose50-99%             -0.5109985  1.3494741  -0.379   0.7049    
stenose70-99%              1.3028582  1.4085773   0.925   0.3550    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1367.4  on 1022  degrees of freedom
Residual deviance: 1325.7  on 1004  degrees of freedom
AIC: 1363.7

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: -0.142011 
Standard error............: 0.066129 
Odds ratio (effect size)..: 0.868 
Lower 95% CI..............: 0.762 
Upper 95% CI..............: 0.988 
Z-value...................: -2.14749 
P-value...................: 0.03175426 
Hosmer and Lemeshow r^2...: 0.030475 
Cox and Snell r^2.........: 0.039915 
Nagelkerke's pseudo r^2...: 0.054139 
Sample size of AE DB......: 2421 
Sample size of model......: 1023 
Missing data %............: 57.74473 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque and serum MCP1, IL6, and IL6R levels and the ‘clinical status’ of the plaque in terms of presence of patients’ symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

  • stroke
  • TIA
  • retinal infarction
  • amaurosis fugax
  • asymptomatic

Model 1

In this model we correct for Age, and Gender.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Quantitative plaque traits


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of MCP1_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
             0.24776               0.26397               0.03239              -0.43507  

Degrees of Freedom: 1195 Total (i.e. Null);  1192 Residual
Null Deviance:      826.5 
Residual Deviance: 804.7    AIC: 812.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5654   0.3738   0.4440   0.5167   0.8434  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)   
(Intercept)           0.24776    0.69360   0.357  0.72093   
currentDF[, PROTEIN]  0.26397    0.09376   2.815  0.00487 **
Age                   0.03239    0.01011   3.203  0.00136 **
Gendermale           -0.43507    0.21792  -1.996  0.04588 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 826.52  on 1195  degrees of freedom
Residual deviance: 804.69  on 1192  degrees of freedom
AIC: 812.69

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.263965 
Standard error............: 0.09376 
Odds ratio (effect size)..: 1.302 
Lower 95% CI..............: 1.083 
Upper 95% CI..............: 1.565 
Z-value...................: 2.815333 
P-value...................: 0.004872671 
Hosmer and Lemeshow r^2...: 0.026418 
Cox and Snell r^2.........: 0.018091 
Nagelkerke's pseudo r^2...: 0.036257 
Sample size of AE DB......: 2421 
Sample size of model......: 1196 
Missing data %............: 50.59893 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Binary plaque traits


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                MedHx_CVD + + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of MCP1_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Med.all.antiplatelet + GFR_MDRD + stenose, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age               Gendermale  Med.all.antiplateletyes                 GFR_MDRD            stenose50-70%  
              15.334896                 0.272930                 0.032194                -0.427140                -0.929815                 0.007921               -13.442880  
          stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
             -15.004949               -14.749153                -0.339553               -15.814682                -0.541412  

Degrees of Freedom: 1035 Total (i.e. Null);  1024 Residual
Null Deviance:      726.5 
Residual Deviance: 689.3    AIC: 713.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + +stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0446   0.3116   0.4390   0.5442   0.9328  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.593e+01  9.540e+02   0.017   0.9867   
currentDF[, PROTEIN]       2.558e-01  1.018e-01   2.512   0.0120 * 
Age                        3.508e-02  1.258e-02   2.788   0.0053 **
Gendermale                -3.507e-01  2.366e-01  -1.482   0.1383   
Hypertension.compositeyes -3.437e-01  3.443e-01  -0.998   0.3182   
DiabetesStatusDiabetes    -3.937e-02  2.423e-01  -0.162   0.8709   
SmokerStatusEx-smoker     -3.416e-01  2.335e-01  -1.463   0.1434   
SmokerStatusNever smoked   2.040e-04  3.564e-01   0.001   0.9995   
Med.Statin.LLDyes         -1.797e-01  2.660e-01  -0.676   0.4991   
Med.all.antiplateletyes   -9.093e-01  4.789e-01  -1.899   0.0576 . 
GFR_MDRD                   7.160e-03  5.456e-03   1.312   0.1894   
BMI                       -1.027e-02  2.789e-02  -0.368   0.7128   
MedHx_CVDyes               9.204e-02  2.096e-01   0.439   0.6606   
stenose50-70%             -1.338e+01  9.540e+02  -0.014   0.9888   
stenose70-90%             -1.495e+01  9.540e+02  -0.016   0.9875   
stenose90-99%             -1.472e+01  9.540e+02  -0.015   0.9877   
stenose100% (Occlusion)   -3.915e-01  1.230e+03   0.000   0.9997   
stenose50-99%             -1.592e+01  9.540e+02  -0.017   0.9867   
stenose70-99%             -5.044e-01  1.190e+03   0.000   0.9997   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 726.47  on 1035  degrees of freedom
Residual deviance: 684.24  on 1017  degrees of freedom
AIC: 722.24

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.255797 
Standard error............: 0.10185 
Odds ratio (effect size)..: 1.291 
Lower 95% CI..............: 1.058 
Upper 95% CI..............: 1.577 
Z-value...................: 2.511506 
P-value...................: 0.01202173 
Hosmer and Lemeshow r^2...: 0.058134 
Cox and Snell r^2.........: 0.039945 
Nagelkerke's pseudo r^2...: 0.079253 
Sample size of AE DB......: 2421 
Sample size of model......: 1036 
Missing data %............: 57.20777 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque and serum MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period.

The primary outcome is defined as “a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death”, i.e. major adverse cardiovascular events (MACE). Variable: epmajor.3years, these include: - myocardial infarction (MI) - cerebral infarction (CVA/stroke) - cardiovascular death (exact cause to be investigated) - cerebral bleeding (CVA/stroke) - fatal myocardial infarction (MI) - fatal cerebral infarction - fatal cerebral bleeding - sudden death - fatal heart failure - fatal aneurysm rupture - other cardiovascular death..

The secondary outcomes will be

  • incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: epstroke.3years, these include:
    • cerebral infarction (CVA/stroke)
    • cerebral bleeding (CVA/stroke)
    • fatal cerebral infarction
    • fatal cerebral bleeding.
  • incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: epcoronary.3years, these include:
    • myocardial infarction (MI)
    • coronary angioplasty (PCI/PTCA)
    • cardiovascular death (exact cause to be investigated)
    • coronary bypass (CABG)
    • fatal myocardial infarction (MI)
    • sudden death.
  • cardiovascular death - variable: epcvdeath.3years, these include:
    • cardiovascular death (exact cause to be investigated)
    • fatal myocardial infarction (MI)
    • fatal cerebral infarction
    • fatal cerebral bleeding
    • sudden death
    • fatal heart failure
    • fatal aneurysm rupture
    • other cardiovascular death..

30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up ‘time-to-event’ variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up.

cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)


rm(AEDB.temp)

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)


rm(AEDB.CEA.temp)

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

  • MACE (epmajor.3years)
  • Stroke (epstroke.3years)
  • Coronary events (epcoronary.3years)
  • Cardiovascular death (epcvdeath.3years)
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

attach(AEDB)
AEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)


rm(AEDB.temp)

attach(AEDB.CEA)
AEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB.CEA)

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)


rm(AEDB.CEA.temp)

Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.

# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
Loading required package: survival
install.packages.auto("survminer")
Loading required package: survminer
install.packages.auto("Hmisc")
Loading required package: Hmisc
Loading required package: lattice
Loading required package: Formula

Attaching package: ‘Hmisc’

The following objects are masked from ‘package:dplyr’:

    src, summarize

The following objects are masked from ‘package:base’:

    format.pval, units
cat("* Creating function to summarize Cox regression and prepare container for results.")
* Creating function to summarize Cox regression and prepare container for results.
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
  cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(protein, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", protein, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}
[1] "Printing the summary of: epmajor.3years"

   0    1 
2033  265 
[1] "Printing the summary of: epstroke.3years"

   0    1 
2169  130 
[1] "Printing the summary of: epcoronary.3years"

   0    1 
2117  182 
[1] "Printing the summary of: epcvdeath.3years"

   0    1 
2208   90 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_3years"
 ep_major_t_3years
 Min.   :0.000    
 1st Qu.:2.708    
 Median :3.000    
 Mean   :2.573    
 3rd Qu.:3.000    
 Max.   :3.000    
 NA's   :125      
[1] "Printing the summary of: ep_stroke_t_3years"
 ep_stroke_t_3years
 Min.   :0.000     
 1st Qu.:2.877     
 Median :3.000     
 Mean   :2.623     
 3rd Qu.:3.000     
 Max.   :3.000     
 NA's   :125       
[1] "Printing the summary of: ep_coronary_t_3years"
 ep_coronary_t_3years
 Min.   :0.000       
 1st Qu.:2.783       
 Median :3.000       
 Mean   :2.622       
 3rd Qu.:3.000       
 Max.   :3.000       
 NA's   :125         
[1] "Printing the summary of: ep_cvdeath_t_3years"
 ep_cvdeath_t_3years
 Min.   :0.00274    
 1st Qu.:2.91233    
 Median :3.00000    
 Mean   :2.70878    
 3rd Qu.:3.00000    
 Max.   :3.00000    
 NA's   :125        
for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_3years"
[1] "Printing the distribution of: ep_stroke_t_3years"
[1] "Printing the distribution of: ep_coronary_t_3years"
[1] "Printing the distribution of: ep_cvdeath_t_3years"

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}
[1] "Printing the summary of: epmajor.30days"

   0    1 
2220   78 
[1] "Printing the summary of: epstroke.30days"

   0    1 
2246   53 
[1] "Printing the summary of: epcoronary.30days"

   0    1 
2265   34 
[1] "Printing the summary of: epcvdeath.30days"

   0    1 
2286   12 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_30days"
 ep_major_t_30days
 Min.   : 0.00    
 1st Qu.:30.00    
 Median :30.00    
 Mean   :29.09    
 3rd Qu.:30.00    
 Max.   :30.00    
 NA's   :125      
[1] "Printing the summary of: ep_stroke_t_30days"
 ep_stroke_t_30days
 Min.   : 0.00     
 1st Qu.:30.00     
 Median :30.00     
 Mean   :29.32     
 3rd Qu.:30.00     
 Max.   :30.00     
 NA's   :125       
[1] "Printing the summary of: ep_coronary_t_30days"
 ep_coronary_t_30days
 Min.   : 0.00       
 1st Qu.:30.00       
 Median :30.00       
 Mean   :29.54       
 3rd Qu.:30.00       
 Max.   :30.00       
 NA's   :125         
[1] "Printing the summary of: ep_cvdeath_t_30days"
 ep_cvdeath_t_30days
 Min.   : 1.001     
 1st Qu.:30.000     
 Median :30.000     
 Mean   :29.854     
 3rd Qu.:30.000     
 Max.   :30.000     
 NA's   :125        
for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_30days"
[1] "Printing the distribution of: ep_stroke_t_30days"
[1] "Printing the distribution of: ep_coronary_t_30days"
[1] "Printing the distribution of: ep_cvdeath_t_30days"

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}
[1] "Printing the summary of: epmajor.90days"

   0    1 
2204   94 
[1] "Printing the summary of: epstroke.90days"

   0    1 
2239   60 
[1] "Printing the summary of: epcoronary.90days"

   0    1 
2255   44 
[1] "Printing the summary of: epcvdeath.90days"

   0    1 
2279   19 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_90days"
 ep_major_t_90days
 Min.   : 0.00    
 1st Qu.:90.00    
 Median :90.00    
 Mean   :86.74    
 3rd Qu.:90.00    
 Max.   :90.00    
 NA's   :125      
[1] "Printing the summary of: ep_stroke_t_90days"
 ep_stroke_t_90days
 Min.   : 0.00     
 1st Qu.:90.00     
 Median :90.00     
 Mean   :87.51     
 3rd Qu.:90.00     
 Max.   :90.00     
 NA's   :125       
[1] "Printing the summary of: ep_coronary_t_90days"
 ep_coronary_t_90days
 Min.   : 0.0        
 1st Qu.:90.0        
 Median :90.0        
 Mean   :88.2        
 3rd Qu.:90.0        
 Max.   :90.0        
 NA's   :125         
[1] "Printing the summary of: ep_cvdeath_t_90days"
 ep_cvdeath_t_90days
 Min.   : 1.001     
 1st Qu.:90.000     
 Median :90.000     
 Mean   :89.320     
 3rd Qu.:90.000     
 Max.   :90.000     
 NA's   :125        
for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_90days"
[1] "Printing the distribution of: ep_stroke_t_90days"
[1] "Printing the distribution of: ep_coronary_t_90days"
[1] "Printing the distribution of: ep_cvdeath_t_90days"

Cox regressions

Let’s perform the actual Cox-regressions. We will apply a couple of models:

  • Model 1: adjusted for age and sex
  • Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

3 years follow-up

MODEL 1

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}
* Analyzing the effect of plaque proteins on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1184, number of events= 139 
   (1237 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)     z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 0.037093  1.037789 0.169988 0.218 0.827268    
Age                                                       0.033469  1.034035 0.009876 3.389 0.000702 ***
Gendermale                                                0.336307  1.399769 0.199676 1.684 0.092131 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055]     1.038     0.9636    0.7437     1.448
Age                                                           1.034     0.9671    1.0142     1.054
Gendermale                                                    1.400     0.7144    0.9464     2.070

Concordance= 0.588  (se = 0.025 )
Likelihood ratio test= 15.11  on 3 df,   p=0.002
Wald test            = 14.36  on 3 df,   p=0.002
Score (logrank) test = 14.43  on 3 df,   p=0.002


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.037093 
Standard error............: 0.169988 
Odds ratio (effect size)..: 1.038 
Lower 95% CI..............: 0.744 
Upper 95% CI..............: 1.448 
T-value...................: 0.218206 
P-value...................: 0.8272683 
Sample size in model......: 1184 
Number of events..........: 139 
* Analyzing the effect of plaque proteins on [epstroke.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1184, number of events= 73 
   (1237 observations deleted due to missingness)

                                                             coef exp(coef) se(coef)     z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 0.13646   1.14621  0.23508 0.580   0.5616  
Age                                                       0.03440   1.03500  0.01356 2.537   0.0112 *
Gendermale                                                0.06281   1.06483  0.25901 0.243   0.8084  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055]     1.146     0.8724    0.7230     1.817
Age                                                           1.035     0.9662    1.0079     1.063
Gendermale                                                    1.065     0.9391    0.6409     1.769

Concordance= 0.597  (se = 0.033 )
Likelihood ratio test= 7.12  on 3 df,   p=0.07
Wald test            = 6.89  on 3 df,   p=0.08
Score (logrank) test = 6.92  on 3 df,   p=0.07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.136457 
Standard error............: 0.235075 
Odds ratio (effect size)..: 1.146 
Lower 95% CI..............: 0.723 
Upper 95% CI..............: 1.817 
T-value...................: 0.580483 
P-value...................: 0.5615888 
Sample size in model......: 1184 
Number of events..........: 73 
* Analyzing the effect of plaque proteins on [epcoronary.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1184, number of events= 91 
   (1237 observations deleted due to missingness)

                                                               coef exp(coef)  se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -0.174454  0.839916  0.210481 -0.829   0.4072  
Age                                                        0.007135  1.007160  0.011848  0.602   0.5471  
Gendermale                                                 0.666235  1.946893  0.269214  2.475   0.0133 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055]    0.8399     1.1906     0.556     1.269
Age                                                          1.0072     0.9929     0.984     1.031
Gendermale                                                   1.9469     0.5136     1.149     3.300

Concordance= 0.577  (se = 0.03 )
Likelihood ratio test= 7.93  on 3 df,   p=0.05
Wald test            = 7.07  on 3 df,   p=0.07
Score (logrank) test = 7.29  on 3 df,   p=0.06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: -0.174454 
Standard error............: 0.210481 
Odds ratio (effect size)..: 0.84 
Lower 95% CI..............: 0.556 
Upper 95% CI..............: 1.269 
T-value...................: -0.828833 
P-value...................: 0.4071991 
Sample size in model......: 1184 
Number of events..........: 91 
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1184, number of events= 45 
   (1237 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -0.11090   0.89502  0.29877 -0.371   0.7105    
Age                                                        0.08444   1.08811  0.01930  4.375 1.21e-05 ***
Gendermale                                                 0.89483   2.44691  0.41200  2.172   0.0299 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055]     0.895     1.1173    0.4983     1.608
Age                                                           1.088     0.9190    1.0477     1.130
Gendermale                                                    2.447     0.4087    1.0913     5.487

Concordance= 0.71  (se = 0.036 )
Likelihood ratio test= 26.84  on 3 df,   p=6e-06
Wald test            = 23.16  on 3 df,   p=4e-05
Score (logrank) test = 23.81  on 3 df,   p=3e-05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: -0.110904 
Standard error............: 0.298774 
Odds ratio (effect size)..: 0.895 
Lower 95% CI..............: 0.498 
Upper 95% CI..............: 1.608 
T-value...................: -0.371199 
P-value...................: 0.7104893 
Sample size in model......: 1184 
Number of events..........: 45 

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

MODEL 2

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}
* Analyzing the effect of plaque proteins on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 1027, number of events= 115 
   (1394 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055]  1.327e-01  1.142e+00  1.898e-01  0.699   0.4843    
Age                                                        3.278e-02  1.033e+00  1.275e-02  2.572   0.0101 *  
Gendermale                                                 3.790e-01  1.461e+00  2.280e-01  1.662   0.0964 .  
Hypertension.compositeno                                  -4.193e-01  6.575e-01  3.564e-01 -1.177   0.2393    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -1.633e-02  9.838e-01  2.234e-01 -0.073   0.9417    
SmokerStatusEx-smoker                                     -5.028e-01  6.049e-01  2.095e-01 -2.400   0.0164 *  
SmokerStatusNever smoked                                  -8.142e-01  4.430e-01  3.415e-01 -2.385   0.0171 *  
Med.Statin.LLDno                                           2.579e-01  1.294e+00  2.155e-01  1.197   0.2314    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     4.283e-01  1.535e+00  2.635e-01  1.626   0.1040    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -1.969e-02  9.805e-01  4.925e-03 -3.997 6.42e-05 ***
BMI                                                        5.402e-02  1.056e+00  2.613e-02  2.067   0.0387 *  
MedHx_CVDyes                                               5.306e-01  1.700e+00  2.220e-01  2.391   0.0168 *  
stenose0-49%                                              -1.560e+01  1.677e-07  2.462e+03 -0.006   0.9949    
stenose50-70%                                             -8.183e-01  4.412e-01  8.701e-01 -0.941   0.3470    
stenose70-90%                                             -2.441e-01  7.834e-01  7.298e-01 -0.335   0.7380    
stenose90-99%                                             -2.079e-01  8.123e-01  7.282e-01 -0.285   0.7753    
stenose100% (Occlusion)                                   -4.754e-02  9.536e-01  1.244e+00 -0.038   0.9695    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.529e+01  2.289e-07  2.920e+03 -0.005   0.9958    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.142e+00  8.757e-01   0.78722    1.6565
Age                                                       1.033e+00  9.678e-01   1.00783    1.0595
Gendermale                                                1.461e+00  6.845e-01   0.93440    2.2840
Hypertension.compositeno                                  6.575e-01  1.521e+00   0.32699    1.3220
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.838e-01  1.016e+00   0.63498    1.5242
SmokerStatusEx-smoker                                     6.049e-01  1.653e+00   0.40120    0.9119
SmokerStatusNever smoked                                  4.430e-01  2.257e+00   0.22685    0.8650
Med.Statin.LLDno                                          1.294e+00  7.727e-01   0.84830    1.9746
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.535e+00  6.516e-01   0.91566    2.5720
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.805e-01  1.020e+00   0.97109    0.9900
BMI                                                       1.056e+00  9.474e-01   1.00281    1.1110
MedHx_CVDyes                                              1.700e+00  5.882e-01   1.10031    2.6266
stenose0-49%                                              1.677e-07  5.962e+06   0.00000       Inf
stenose50-70%                                             4.412e-01  2.267e+00   0.08017    2.4278
stenose70-90%                                             7.834e-01  1.276e+00   0.18741    3.2747
stenose90-99%                                             8.123e-01  1.231e+00   0.19494    3.3848
stenose100% (Occlusion)                                   9.536e-01  1.049e+00   0.08333   10.9119
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.289e-07  4.369e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.697  (se = 0.023 )
Likelihood ratio test= 63.79  on 18 df,   p=5e-07
Wald test            = 58.83  on 18 df,   p=3e-06
Score (logrank) test = 62.19  on 18 df,   p=9e-07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.132746 
Standard error............: 0.189795 
Odds ratio (effect size)..: 1.142 
Lower 95% CI..............: 0.787 
Upper 95% CI..............: 1.657 
T-value...................: 0.699416 
P-value...................: 0.4842918 
Sample size in model......: 1027 
Number of events..........: 115 
* Analyzing the effect of plaque proteins on [epstroke.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 1027, number of events= 59 
   (1394 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055]  1.934e-01  1.213e+00  2.668e-01  0.725   0.4684  
Age                                                        4.267e-02  1.044e+00  1.756e-02  2.429   0.0151 *
Gendermale                                                -4.383e-02  9.571e-01  2.992e-01 -0.146   0.8835  
Hypertension.compositeno                                   8.567e-03  1.009e+00  4.178e-01  0.021   0.9836  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                    -2.315e-02  9.771e-01  3.168e-01 -0.073   0.9418  
SmokerStatusEx-smoker                                     -1.168e-01  8.898e-01  2.964e-01 -0.394   0.6936  
SmokerStatusNever smoked                                  -9.611e-01  3.825e-01  5.239e-01 -1.835   0.0666 .
Med.Statin.LLDno                                           3.743e-01  1.454e+00  2.926e-01  1.279   0.2008  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     3.817e-01  1.465e+00  3.714e-01  1.028   0.3040  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -4.331e-03  9.957e-01  6.992e-03 -0.619   0.5357  
BMI                                                        8.171e-02  1.085e+00  3.459e-02  2.362   0.0182 *
MedHx_CVDyes                                               3.652e-01  1.441e+00  2.940e-01  1.242   0.2142  
stenose0-49%                                              -1.523e+01  2.419e-07  3.388e+03 -0.004   0.9964  
stenose50-70%                                             -5.088e-01  6.012e-01  1.160e+00 -0.438   0.6610  
stenose70-90%                                             -2.645e-01  7.676e-01  1.029e+00 -0.257   0.7971  
stenose90-99%                                             -2.597e-01  7.713e-01  1.029e+00 -0.252   0.8008  
stenose100% (Occlusion)                                    5.974e-01  1.817e+00  1.439e+00  0.415   0.6780  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                             -1.515e+01  2.629e-07  3.962e+03 -0.004   0.9969  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.213e+00  8.242e-01   0.71934     2.047
Age                                                       1.044e+00  9.582e-01   1.00828     1.080
Gendermale                                                9.571e-01  1.045e+00   0.53242     1.721
Hypertension.compositeno                                  1.009e+00  9.915e-01   0.44469     2.288
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.771e-01  1.023e+00   0.52513     1.818
SmokerStatusEx-smoker                                     8.898e-01  1.124e+00   0.49772     1.591
SmokerStatusNever smoked                                  3.825e-01  2.615e+00   0.13699     1.068
Med.Statin.LLDno                                          1.454e+00  6.878e-01   0.81941     2.580
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.465e+00  6.827e-01   0.70740     3.033
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.957e-01  1.004e+00   0.98213     1.009
BMI                                                       1.085e+00  9.215e-01   1.01402     1.161
MedHx_CVDyes                                              1.441e+00  6.940e-01   0.80970     2.564
stenose0-49%                                              2.419e-07  4.133e+06   0.00000       Inf
stenose50-70%                                             6.012e-01  1.663e+00   0.06185     5.844
stenose70-90%                                             7.676e-01  1.303e+00   0.10217     5.767
stenose90-99%                                             7.713e-01  1.297e+00   0.10256     5.800
stenose100% (Occlusion)                                   1.817e+00  5.503e-01   0.10830    30.495
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.629e-07  3.804e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.67  (se = 0.034 )
Likelihood ratio test= 23.02  on 18 df,   p=0.2
Wald test            = 20.97  on 18 df,   p=0.3
Score (logrank) test = 22.15  on 18 df,   p=0.2


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.1934 
Standard error............: 0.26675 
Odds ratio (effect size)..: 1.213 
Lower 95% CI..............: 0.719 
Upper 95% CI..............: 2.047 
T-value...................: 0.725021 
P-value...................: 0.4684393 
Sample size in model......: 1027 
Number of events..........: 59 
* Analyzing the effect of plaque proteins on [epcoronary.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 1027, number of events= 78 
   (1394 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -1.290e-01  8.790e-01  2.315e-01 -0.557 0.577222    
Age                                                       -8.243e-04  9.992e-01  1.506e-02 -0.055 0.956352    
Gendermale                                                 8.625e-01  2.369e+00  3.029e-01  2.848 0.004404 ** 
Hypertension.compositeno                                  -9.192e-01  3.989e-01  5.209e-01 -1.765 0.077619 .  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -1.045e-01  9.008e-01  2.759e-01 -0.379 0.704879    
SmokerStatusEx-smoker                                     -6.291e-01  5.331e-01  2.573e-01 -2.445 0.014496 *  
SmokerStatusNever smoked                                  -2.735e-01  7.607e-01  3.647e-01 -0.750 0.453241    
Med.Statin.LLDno                                           1.147e-01  1.122e+00  2.730e-01  0.420 0.674465    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     3.664e-01  1.443e+00  3.348e-01  1.095 0.273710    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -2.059e-02  9.796e-01  5.940e-03 -3.466 0.000528 ***
BMI                                                        1.350e-02  1.014e+00  3.290e-02  0.410 0.681500    
MedHx_CVDyes                                               6.867e-01  1.987e+00  2.795e-01  2.457 0.014000 *  
stenose0-49%                                              -1.584e+01  1.320e-07  3.048e+03 -0.005 0.995853    
stenose50-70%                                             -1.713e+00  1.803e-01  1.418e+00 -1.208 0.227098    
stenose70-90%                                             -1.372e-01  8.718e-01  1.022e+00 -0.134 0.893141    
stenose90-99%                                             -1.918e-01  8.255e-01  1.022e+00 -0.188 0.851135    
stenose100% (Occlusion)                                   -1.540e+01  2.059e-07  2.452e+03 -0.006 0.994990    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                              8.142e-01  2.257e+00  1.429e+00  0.570 0.568977    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 8.790e-01  1.138e+00   0.55840    1.3835
Age                                                       9.992e-01  1.001e+00   0.97011    1.0291
Gendermale                                                2.369e+00  4.221e-01   1.30849    4.2894
Hypertension.compositeno                                  3.989e-01  2.507e+00   0.14370    1.1071
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.008e-01  1.110e+00   0.52459    1.5468
SmokerStatusEx-smoker                                     5.331e-01  1.876e+00   0.32192    0.8827
SmokerStatusNever smoked                                  7.607e-01  1.315e+00   0.37224    1.5546
Med.Statin.LLDno                                          1.122e+00  8.917e-01   0.65677    1.9151
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.443e+00  6.932e-01   0.74849    2.7802
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.796e-01  1.021e+00   0.96828    0.9911
BMI                                                       1.014e+00  9.866e-01   0.95030    1.0811
MedHx_CVDyes                                              1.987e+00  5.032e-01   1.14909    3.4365
stenose0-49%                                              1.320e-07  7.576e+06   0.00000       Inf
stenose50-70%                                             1.803e-01  5.547e+00   0.01118    2.9061
stenose70-90%                                             8.718e-01  1.147e+00   0.11772    6.4559
stenose90-99%                                             8.255e-01  1.211e+00   0.11137    6.1183
stenose100% (Occlusion)                                   2.059e-07  4.857e+06   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.257e+00  4.430e-01   0.13703   37.1835
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.726  (se = 0.028 )
Likelihood ratio test= 49.9  on 18 df,   p=8e-05
Wald test            = 43.46  on 18 df,   p=7e-04
Score (logrank) test = 47.19  on 18 df,   p=2e-04


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: -0.129025 
Standard error............: 0.231458 
Odds ratio (effect size)..: 0.879 
Lower 95% CI..............: 0.558 
Upper 95% CI..............: 1.384 
T-value...................: -0.557447 
P-value...................: 0.577222 
Sample size in model......: 1027 
Number of events..........: 78 
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 1027, number of events= 33 
   (1394 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -2.044e-01  8.152e-01  3.583e-01 -0.570  0.56836    
Age                                                        6.640e-02  1.069e+00  2.655e-02  2.501  0.01238 *  
Gendermale                                                 1.258e+00  3.520e+00  5.565e-01  2.261  0.02373 *  
Hypertension.compositeno                                  -1.774e+01  1.978e-08  4.017e+03 -0.004  0.99648    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -3.835e-02  9.624e-01  4.261e-01 -0.090  0.92829    
SmokerStatusEx-smoker                                     -5.672e-01  5.671e-01  4.021e-01 -1.411  0.15834    
SmokerStatusNever smoked                                  -4.302e-01  6.504e-01  6.158e-01 -0.699  0.48479    
Med.Statin.LLDno                                           8.681e-02  1.091e+00  4.156e-01  0.209  0.83454    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.130e+00  3.096e+00  4.161e-01  2.716  0.00661 ** 
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -3.395e-02  9.666e-01  9.369e-03 -3.624  0.00029 ***
BMI                                                        8.410e-02  1.088e+00  5.118e-02  1.643  0.10031    
MedHx_CVDyes                                               7.683e-01  2.156e+00  4.605e-01  1.668  0.09528 .  
stenose0-49%                                              -2.031e+01  1.509e-09  2.713e+04 -0.001  0.99940    
stenose50-70%                                             -1.041e+00  3.531e-01  1.236e+00 -0.842  0.39970    
stenose70-90%                                             -1.480e+00  2.276e-01  1.069e+00 -1.385  0.16609    
stenose90-99%                                             -1.069e+00  3.434e-01  1.054e+00 -1.014  0.31055    
stenose100% (Occlusion)                                   -1.957e+01  3.154e-09  1.974e+04 -0.001  0.99921    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.939e+01  3.802e-09  3.407e+04 -0.001  0.99955    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 8.152e-01  1.227e+00   0.40392    1.6451
Age                                                       1.069e+00  9.358e-01   1.01447    1.1257
Gendermale                                                3.520e+00  2.841e-01   1.18264   10.4751
Hypertension.compositeno                                  1.978e-08  5.054e+07   0.00000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.624e-01  1.039e+00   0.41748    2.2185
SmokerStatusEx-smoker                                     5.671e-01  1.763e+00   0.25789    1.2471
SmokerStatusNever smoked                                  6.504e-01  1.538e+00   0.19453    2.1744
Med.Statin.LLDno                                          1.091e+00  9.168e-01   0.48298    2.4631
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    3.096e+00  3.230e-01   1.36967    6.9988
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.666e-01  1.035e+00   0.94903    0.9845
BMI                                                       1.088e+00  9.193e-01   0.98393    1.2025
MedHx_CVDyes                                              2.156e+00  4.638e-01   0.87427    5.3170
stenose0-49%                                              1.509e-09  6.625e+08   0.00000       Inf
stenose50-70%                                             3.531e-01  2.832e+00   0.03132    3.9813
stenose70-90%                                             2.276e-01  4.394e+00   0.02802    1.8490
stenose90-99%                                             3.434e-01  2.912e+00   0.04351    2.7102
stenose100% (Occlusion)                                   3.154e-09  3.170e+08   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             3.802e-09  2.630e+08   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.839  (se = 0.031 )
Likelihood ratio test= 60.15  on 18 df,   p=2e-06
Wald test            = 20.83  on 18 df,   p=0.3
Score (logrank) test = 56.03  on 18 df,   p=9e-06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: -0.204374 
Standard error............: 0.358254 
Odds ratio (effect size)..: 0.815 
Lower 95% CI..............: 0.404 
Upper 95% CI..............: 1.645 
T-value...................: -0.570471 
P-value...................: 0.568358 
Sample size in model......: 1027 
Number of events..........: 33 

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)
object 'head.style' not found

30-days follow-up

MODEL 1

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

MODEL 2

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

90-days follow-up

MODEL 1

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

MODEL 2

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

Correlations

All biomarkers

We correlated serum and plaque levels of the biomarkers.


# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")
Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
  Use `force = TRUE` to force installation
library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_pg_ug_2015_rank",
                                               TRAITS.BIN, TRAITS.CON.RANK)
                                    )


AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)
tibble [2,421 × 8] (S3: tbl_df/tbl/data.frame)
 $ MCP1_pg_ug_2015_rank: num [1:2421] NA 0.937 2.158 1.209 1.996 ...
 $ CalcificationPlaque : num [1:2421] 1 1 1 1 1 2 2 1 2 1 ...
 $ CollagenPlaque      : num [1:2421] 1 2 2 2 2 1 NA 2 2 2 ...
 $ Fat10Perc           : num [1:2421] 2 2 2 2 2 2 2 2 2 2 ...
 $ IPH                 : num [1:2421] 2 2 2 2 1 2 2 2 2 2 ...
 $ Macrophages_rank    : num [1:2421] 1.388 1.12 1.365 0.721 0.396 ...
 $ SMC_rank            : num [1:2421] 1.67023 1.13089 0.00295 1.42623 1.2689 ...
 $ VesselDensity_rank  : num [1:2421] -0.529 -0.977 -0.774 0.716 1.099 ...
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")
Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))



# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)


# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)



# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:

# ggpairs(AEDB.CEA, 
#         columns = c("MCP1_rank", "MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK), 
#         columnLabels = c("MCP1 (serum)", "MCP1", 
#                          "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
#         method = c("spearman"),
#         # ggplot2::aes(colour = Gender),
#         progress = FALSE) 

ggpairs(AEDB.CEA,
        columns = c("MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK),
        columnLabels = c("MCP1",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE)
Extra arguments: 'method' are being ignored.  If these are meant to be aesthetics, submit them using the 'mapping' variable within ggpairs with ggplot2::aes or ggplot2::aes_string.

Circulating MCP1

Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:

  1. A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
  2. Associations of circulating MCP-1 levels with plaque vulnerability characteristics
  3. Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
  4. Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.

NOT AVAILABLE YET


# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_rank", 
                                     TRAITS.BIN, TRAITS.CON.RANK, 
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers_serum.rank <- round(cor(AEDB.CEA.matrix.serum.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_serum_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_serum_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}


corr_biomarkers_serum.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum.rank, corr_biomarkers_serum_p.rank))
DT::datatable(corr_biomarkers_serum.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AEDB.CEA.matrix.serum.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA,
        columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"), 
        columnLabels = c("MCP1 (serum)", 
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 

Additional figures

Age and sex

We want to create per-age-group figures.

  • Box and Whisker plot for MCP-1 plaque levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
  • Box and Whisker plot for MCP-1 serum levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
  • Scatter plot of the correlation and regression line between MCP-1 levels in plaque (y axis) and serum (x axis).
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AEDB.CEA$AgeGroup, AEDB.CEA$Gender)
       
        female male
  <55       45   98
  55-64    193  410
  65-74    264  687
  75-84    202  438
  85+       34   50
table(AEDB.CEA$AgeGroupSex)

   <55 females      <55 males 55-64 females     55-64 males  65-74 females    65-74 males  75-84 females    75-84 males    85+ females 
            45             98            193            410            264            687            202            438             34 
     85+ males 
            50 

Now we can draw some graphs of serum/plaque MCP1 levels per sex and age group.

Inverse-rank transformed data

MCP1 plaque levels


# ?ggpubr::ggboxplot()

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

MCP1 serum levels

NOT AVAILABLE YET

# compare_means(MCP1_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("Gender"),
                  y = "MCP1_rank",
                  xlab = "Gender",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("AgeGroup"),
                  y = "MCP1_rank",
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

Raw data

Simalarly but now for the raw data as median ± interquartile range.

MCP1 plaque levels


# ?ggpubr::ggboxplot()

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+

  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+

  # stat_compare_means(method = "kruskal.test")

MCP1 serum levels

NOT AVAILABLE YET

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1", 
                  xlab = "Gender",
                  ylab = "MCP1 serum [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 serum [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

Circulating vs. plaque MCP1 levels

We will also make a nice correlation plot between serum and plaque MCP1 levels.

NOT AVAILABLE YET


ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015",
                  y = "MCP1", 
                  xlab = "MCP1 plaque [pg/ug]",
                  ylab = "MCP1 serum [pg/mL]",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(8,750))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015_rank",
                  y = "MCP1_rank", 
                  xlab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(2,3))

Plaque vs. plaque MCP1 levels

We will also make a nice correlation plot between the two experiments of plaque MCP1 levels.

AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1", 
                  y = "MCP1_pg_ug_2015",
                  xlab = "MCP1 plaque [pg/mL] (exp. no. 1)",
                  ylab = "MCP1 plaque [pg/ug] (exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(8,750))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_rank", 
                  y = "MCP1_pg_ug_2015_rank",
                  xlab = "MCP1 plaque [pg/mL]\n(INRT,  exp. no. 1)",
                  ylab = "MCP1 plaque [pg/ug]\n(INRT,  exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(2,3))

Symptoms

We want to create per-symptom figures.

library(dplyr)

table(AEDB.CEA$AgeGroup, AEDB.CEA$AsymptSympt2G)
       
        Asymptomatic Symptomatic
  <55             24         119
  55-64           76         527
  65-74          124         827
  75-84           43         597
  85+              3          81
table(AEDB.CEA$Gender, AEDB.CEA$AsymptSympt2G)
        
         Asymptomatic Symptomatic
  female           64         674
  male            206        1477
table(AEDB.CEA$AsymptSympt2G)

Asymptomatic  Symptomatic 
         270         2151 

Now we can draw some graphs of serum/plaque MCP1 levels per symptom group.


# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_pg_ug_2015_rank",
                  title = "MCP1 plaque [pg/ug] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 plaque [pg/ug]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_rank",
                  title = "MCP1 serum [pg/mL] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 serum [pg/mL]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
rm(p1)

Forest plots

We would also like to visualize the multivariable analyses results.

library(ggplot2)
library(openxlsx)
model1_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"))
model2_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"))
model1_mcp1$model <- "univariate"
model2_mcp1$model <- "multivariate"

models_mcp1 <- rbind(model1_mcp1, model2_mcp1)
models_mcp1
NA
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]))

fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
  geom_pointrange() + 
  geom_hline(yintercept=1, lty=2) +  # add a dotted line at x=1 after flip
  coord_flip() +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  theme(text = element_text(size=14)) +
  ggtitle("Plaque MCP-1 levels (1 SD increment)") +
  theme_minimal()  # use a white background
print(fp)

rm(fp)
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_rank"]))

fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
  geom_pointrange() + 
  geom_hline(yintercept=1, lty=2) +  # add a dotted line at x=1 after flip
  coord_flip() +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  theme(text = element_text(size=14)) +
  ggtitle("Serum MCP-1 levels (1 SD increment)") +
  theme_minimal()  # use a white background
print(fp)
rm(fp)

MCP1 vs. cytokines plaque levels

We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:

  • IL2
  • IL4
  • IL5
  • IL6
  • IL8
  • IL9
  • IL10
  • IL12
  • IL13
  • IL21
  • INFG
  • TNFA
  • MIF
  • MCP1
  • MIP1a
  • RANTES
  • MIG
  • IP10
  • Eotaxin1
  • TARC
  • PARC
  • MDC
  • OPG
  • sICAM1
  • VEGFA
  • TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay.

  • MMP2
  • MMP8
  • MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the MCP1 plaque levels.

cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")

# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"


proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))


# make variables numerics()
AEDB.CEA <- AEDB.CEA %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
funs() is soft deprecated as of dplyr 0.8.0
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once per session.Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(proteins_of_interest)` instead of `proteins_of_interest` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
  
for(PROTEIN in 1:length(proteins_of_interest)){

  # UCORBIOGSAqc$Z <- NULL
  var.temp.rank = proteins_of_interest_rank[PROTEIN]
  var.temp = proteins_of_interest[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  # AEDB.CEA <-  AEDB.CEA %>% mutate(AEDB.CEA[,var.temp] == replace(AEDB.CEA[,var.temp], AEDB.CEA[,var.temp]==0, NA))

  AEDB.CEA[,var.temp][AEDB.CEA[,var.temp]==0.000000]=NA

  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AEDB.CEA[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AEDB.CEA[,var.temp])),").\n"))
  
  AEDB.CEA <- AEDB.CEA %>%
      mutate_at(vars(var.temp), 
        # list(Z = ~ (AEDB.CEA[,var.temp] - mean(AEDB.CEA[,var.temp], na.rm = TRUE))/sd(AEDB.CEA[,var.temp], na.rm = TRUE))
        list(RANK = ~ qnorm((rank(AEDB.CEA[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA[,var.temp]))))
      )
  # str(UCORBIOGSAqc$Z)
  cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
  AEDB.CEA[,var.temp.rank] <- NULL
  names(AEDB.CEA)[names(AEDB.CEA) == "RANK"] <- var.temp.rank
}

Selecting IL2 and standardising: IL2_rank.
* changing IL2 to numeric.
* standardising IL2 (mean: 0.180091, n = 436).
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(var.temp)` instead of `var.temp` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
* renaming RANK to IL2_rank.

Selecting IL4 and standardising: IL4_rank.
* changing IL4 to numeric.
* standardising IL4 (mean: 0.167699, n = 406).
* renaming RANK to IL4_rank.

Selecting IL5 and standardising: IL5_rank.
* changing IL5 to numeric.
* standardising IL5 (mean: 0.178439, n = 432).
* renaming RANK to IL5_rank.

Selecting IL6 and standardising: IL6_rank.
* changing IL6 to numeric.
* standardising IL6 (mean: 0.188352, n = 456).
* renaming RANK to IL6_rank.

Selecting IL8 and standardising: IL8_rank.
* changing IL8 to numeric.
* standardising IL8 (mean: 0.182156, n = 441).
* renaming RANK to IL8_rank.

Selecting IL9 and standardising: IL9_rank.
* changing IL9 to numeric.
* standardising IL9 (mean: 0.206526, n = 500).
* renaming RANK to IL9_rank.

Selecting IL10 and standardising: IL10_rank.
* changing IL10 to numeric.
* standardising IL10 (mean: 0.159025, n = 385).
* renaming RANK to IL10_rank.

Selecting IL12 and standardising: IL12_rank.
* changing IL12 to numeric.
* standardising IL12 (mean: 0.168112, n = 407).
* renaming RANK to IL12_rank.

Selecting IL13 and standardising: IL13_rank.
* changing IL13 to numeric.
* standardising IL13 (mean: 0.232962, n = 564).
* renaming RANK to IL13_rank.

Selecting IL21 and standardising: IL21_rank.
* changing IL21 to numeric.
* standardising IL21 (mean: 0.233375, n = 565).
* renaming RANK to IL21_rank.

Selecting INFG and standardising: INFG_rank.
* changing INFG to numeric.
* standardising INFG (mean: 0.179265, n = 434).
* renaming RANK to INFG_rank.

Selecting TNFA and standardising: TNFA_rank.
* changing TNFA to numeric.
* standardising TNFA (mean: 0.163569, n = 396).
* renaming RANK to TNFA_rank.

Selecting MIF and standardising: MIF_rank.
* changing MIF to numeric.
* standardising MIF (mean: 0.233375, n = 565).
* renaming RANK to MIF_rank.

Selecting MCP1 and standardising: MCP1_rank.
* changing MCP1 to numeric.
* standardising MCP1 (mean: 0.229657, n = 556).
* renaming RANK to MCP1_rank.

Selecting MIP1a and standardising: MIP1a_rank.
* changing MIP1a to numeric.
* standardising MIP1a (mean: 0.211896, n = 513).
* renaming RANK to MIP1a_rank.

Selecting RANTES and standardising: RANTES_rank.
* changing RANTES to numeric.
* standardising RANTES (mean: 0.228831, n = 554).
* renaming RANK to RANTES_rank.

Selecting MIG and standardising: MIG_rank.
* changing MIG to numeric.
* standardising MIG (mean: 0.227179, n = 550).
* renaming RANK to MIG_rank.

Selecting IP10 and standardising: IP10_rank.
* changing IP10 to numeric.
* standardising IP10 (mean: 0.206113, n = 499).
* renaming RANK to IP10_rank.

Selecting Eotaxin1 and standardising: Eotaxin1_rank.
* changing Eotaxin1 to numeric.
* standardising Eotaxin1 (mean: 0.233375, n = 565).
* renaming RANK to Eotaxin1_rank.

Selecting TARC and standardising: TARC_rank.
* changing TARC to numeric.
* standardising TARC (mean: 0.200743, n = 486).
* renaming RANK to TARC_rank.

Selecting PARC and standardising: PARC_rank.
* changing PARC to numeric.
* standardising PARC (mean: 0.233375, n = 565).
* renaming RANK to PARC_rank.

Selecting MDC and standardising: MDC_rank.
* changing MDC to numeric.
* standardising MDC (mean: 0.209831, n = 508).
* renaming RANK to MDC_rank.

Selecting OPG and standardising: OPG_rank.
* changing OPG to numeric.
* standardising OPG (mean: 0.232962, n = 564).
* renaming RANK to OPG_rank.

Selecting sICAM1 and standardising: sICAM1_rank.
* changing sICAM1 to numeric.
* standardising sICAM1 (mean: 0.233375, n = 565).
* renaming RANK to sICAM1_rank.

Selecting VEGFA and standardising: VEGFA_rank.
* changing VEGFA to numeric.
* standardising VEGFA (mean: 0.20157, n = 488).
* renaming RANK to VEGFA_rank.

Selecting TGFB and standardising: TGFB_rank.
* changing TGFB to numeric.
* standardising TGFB (mean: 0.23007, n = 557).
* renaming RANK to TGFB_rank.

Selecting MMP2 and standardising: MMP2_rank.
* changing MMP2 to numeric.
* standardising MMP2 (mean: 0.232135, n = 562).
* renaming RANK to MMP2_rank.

Selecting MMP8 and standardising: MMP8_rank.
* changing MMP8 to numeric.
* standardising MMP8 (mean: 0.232135, n = 562).
* renaming RANK to MMP8_rank.

Selecting MMP9 and standardising: MMP9_rank.
* changing MMP9 to numeric.
* standardising MMP9 (mean: 0.231722, n = 561).
* renaming RANK to MMP9_rank.
# rm(var.temp, var.temp.rank)

We will just visualize these transformations.

proteins_of_interest_rank_mcp1 <- c("MCP1_pg_ug_2015_rank", proteins_of_interest_rank)

proteins_of_interest_mcp1 <- c("MCP1_pg_ug_2015", proteins_of_interest)

for(PROTEIN in proteins_of_interest_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "",
                    ggtheme = theme_minimal())
  print(p1)
  
}
Plotting protein MCP1_pg_ug_2015.
Using `bins = 30` by default. Pick better value with the argument `bins`.
Plotting protein IL2.
Plotting protein IL4.
Plotting protein IL5.
Plotting protein IL6.
Plotting protein IL8.
Plotting protein IL9.
Plotting protein IL10.
Plotting protein IL12.
Plotting protein IL13.
Plotting protein IL21.
Plotting protein INFG.
Plotting protein TNFA.
Plotting protein MIF.
Plotting protein MCP1.
Plotting protein MIP1a.
Plotting protein RANTES.
Plotting protein MIG.
Plotting protein IP10.
Plotting protein Eotaxin1.
Plotting protein TARC.
Plotting protein PARC.
Plotting protein MDC.
Plotting protein OPG.
Plotting protein sICAM1.
Plotting protein VEGFA.
Plotting protein TGFB.
Plotting protein MMP2.
Plotting protein MMP8.
Plotting protein MMP9.

for(PROTEIN in proteins_of_interest_rank_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "inverse-normal transformation",
                    ggtheme = theme_minimal())
  print(p1)
  
}
Plotting protein MCP1_pg_ug_2015_rank.
Using `bins = 30` by default. Pick better value with the argument `bins`.
Plotting protein IL2_rank.
Plotting protein IL4_rank.
Plotting protein IL5_rank.
Plotting protein IL6_rank.
Plotting protein IL8_rank.
Plotting protein IL9_rank.
Plotting protein IL10_rank.
Plotting protein IL12_rank.
Plotting protein IL13_rank.
Plotting protein IL21_rank.
Plotting protein INFG_rank.
Plotting protein TNFA_rank.
Plotting protein MIF_rank.
Plotting protein MCP1_rank.
Plotting protein MIP1a_rank.
Plotting protein RANTES_rank.
Plotting protein MIG_rank.
Plotting protein IP10_rank.
Plotting protein Eotaxin1_rank.
Plotting protein TARC_rank.
Plotting protein PARC_rank.
Plotting protein MDC_rank.
Plotting protein OPG_rank.
Plotting protein sICAM1_rank.
Plotting protein VEGFA_rank.
Plotting protein TGFB_rank.
Plotting protein MMP2_rank.
Plotting protein MMP8_rank.
Plotting protein MMP9_rank.

NA
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")
Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
  Use `force = TRUE` to force installation
library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c(proteins_of_interest_rank_mcp1)
                                    )

# str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

rename_proteins_of_interest_mcp1 <- c("MCP1 (L, exp2)", 
                                    "IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", 
                                    "IL13 (L)", "IL21 (L)", 
                                    "INFG", "TNFA", "MIF (L)", 
                                    "MCP1 (L, exp1)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)", 
                                    "Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)", 
                                    "OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (r)", "MMP8 (r)", "MMP9 (r)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           tl.cex = 6,
           # xlab = c("MCP1"),
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

Session information


Version:      v1.0.8
Last update:  2020-06-25
Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description:  Script to analyse MCP1 from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_
* analysis on serum

_S_
* prettify forest plot

_C_


_W_


**Changes log**
* v1.0.8 Fixed error in MCP1 serum analysis. It turns out the `MCP1` and `MCP1_pg_ug_2015` variables are _both_ measured in plaque, in two separate experiments, exp. no. 1 and exp. no. 2, respectively. 
* v1.0.7 Fixed the per Age-group MCP1 Box plots. Added correlations with other cytokines in plaques.
* v1.0.6 Only analyses and figures that end up in the final manuscript.
* v1.0.5 Update with 30- and 90-days survival.
* v1.0.4 Updated with Cox-regressions.
* v1.0.3 Included more models.
* v1.0.2 Bugs fixed.
* v1.0.1 Extended with linear and logistic regressions
* v1.0.0 Inital version

sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin19.4.0 (64-bit)
Running under: macOS Catalina 10.15.5

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /usr/local/Cellar/openblas/0.3.10/lib/libopenblasp-r0.3.10.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] tools     stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] openxlsx_4.1.5             GGally_1.5.0               PerformanceAnalytics_2.0.4 xts_0.12-0                 zoo_1.8-8                 
 [6] ggpubr_0.3.0               tableone_0.11.1            labelled_2.4.0             sjPlot_2.8.4               sjlabelled_1.1.5          
[11] haven_2.3.0                MASS_7.3-51.6              DT_0.13                    knitr_1.28                 forcats_0.5.0             
[16] stringr_1.4.0              purrr_0.3.4                tibble_3.0.1               tidyverse_1.3.0            data.table_1.12.8         
[21] naniar_0.5.1               tidyr_1.1.0                dplyr_0.8.5                optparse_1.6.6             readr_1.3.1               
[26] ggcorrplot_0.1.3.999       ggplot2_3.3.0              devtools_2.3.0             usethis_1.6.1             

loaded via a namespace (and not attached):
  [1] readxl_1.3.1       backports_1.1.7    plyr_1.8.6         splines_3.6.3      crosstalk_1.1.0.1  TH.data_1.0-10     digest_0.6.25     
  [8] htmltools_0.4.0    fansi_0.4.1        magrittr_1.5       memoise_1.1.0      remotes_2.1.1      modelr_0.1.8       sandwich_2.5-1    
 [15] prettyunits_1.1.1  colorspace_1.4-1   rvest_0.3.5        mitools_2.4        xfun_0.14          callr_3.4.3        crayon_1.3.4      
 [22] jsonlite_1.6.1     lme4_1.1-23        survival_3.1-12    glue_1.4.1         gtable_0.3.0       emmeans_1.4.7      sjstats_0.18.0    
 [29] sjmisc_2.8.4       car_3.0-8          pkgbuild_1.0.8     abind_1.4-5        scales_1.1.1       mvtnorm_1.1-0      DBI_1.1.0         
 [36] rstatix_0.5.0.999  ggeffects_0.14.3   Rcpp_1.0.4.6       xtable_1.8-4       performance_0.4.6  foreign_0.8-75     survey_4.0        
 [43] htmlwidgets_1.5.1  httr_1.4.1         getopt_1.20.3      RColorBrewer_1.1-2 ellipsis_0.3.1     reshape_0.8.8      pkgconfig_2.0.3   
 [50] farver_2.0.3       dbplyr_1.4.3       tidyselect_1.1.0   labeling_0.3       rlang_0.4.6        reshape2_1.4.4     effectsize_0.3.1  
 [57] munsell_0.5.0      cellranger_1.1.0   cli_2.0.2          generics_0.0.2     broom_0.5.6        evaluate_0.14      yaml_2.2.1        
 [64] processx_3.4.2     fs_1.4.1           zip_2.0.4          packrat_0.5.0      visdat_0.5.3       nlme_3.1-148       xml2_1.3.2        
 [71] compiler_3.6.3     rstudioapi_0.11    curl_4.3           testthat_2.3.2     ggsignif_0.6.0     reprex_0.3.0       statmod_1.4.34    
 [78] stringi_1.4.6      ps_1.3.3           parameters_0.7.0   desc_1.2.0         lattice_0.20-41    Matrix_1.2-18      nloptr_1.2.2.1    
 [85] vctrs_0.3.0        pillar_1.4.4       lifecycle_0.2.0    estimability_1.3   insight_0.8.4      R6_2.4.1           rio_0.5.16        
 [92] sessioninfo_1.1.1  codetools_0.2-16   boot_1.3-25        assertthat_0.2.1   pkgload_1.0.2      rprojroot_1.3-2    withr_2.2.0       
 [99] multcomp_1.4-13    mgcv_1.8-31        bayestestR_0.6.0   hms_0.5.3          quadprog_1.5-8     grid_3.6.3         coda_0.19-3       
[106] minqa_1.2.4        rmarkdown_2.1      carData_3.0-4      base64enc_0.1-3    lubridate_1.7.8   

Saving environment

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))
© 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | swvanderlaan.github.io.
---
title: "Athero-Express Biobank Study -- IL6 and MCP1 plaque levels."
author: '[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans'
date: '`r Sys.Date()`'
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Helvetica
subtitle: An 'Athero-Express Biobank Study' project
editor_options:
  chunk_output_type: inline
---
```{r global_options, include = FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
                      eval = TRUE, warning = FALSE, message = FALSE)
```

# Preparation

Clean the environment.
```{r ClearEnvironment, include = FALSE}
rm(list = ls())
```

Set locations, and the working directory.
```{r LocalSystem, include = FALSE}
### Operating System Version
### Mac Pro
# ROOT_loc = "/Volumes/EliteProQx2Media"
# GENOMIC_loc = "/Users/svanderlaan/iCloud/Genomics"

### MacBook
ROOT_loc = "/Users/swvanderlaan"
GENOMIC_loc = paste0(ROOT_loc, "/iCloud/Genomics")

### Generic Locations
AEDB_loc = paste0(GENOMIC_loc, "/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
RESULTS = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")
RAWDATA = paste0(ROOT_loc, "/PLINK/_AE_ORIGINALS/AESCRNA/prepped_data")
PROJECT_loc = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")

### SOME VARIABLES WE NEED DOWN THE LINE
cat("\nDefining phenotypes and datasets.\n")
PROJECTNAME="IL6MCP1"
# SUBPROJECTNAME=""

cat("\nCreate a new analysis directory, including subdirectories.\n")
# Analysis
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

# Plots
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

# QC plots
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

# Output files
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

# COX analysis
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/COX")), 
       dir.create(file.path(ANALYSIS_loc, "/COX")), 
       FALSE)
COX_loc = paste0(ANALYSIS_loc, "/COX")

# Baseline characteristics
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")

# Sample selection
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       dir.create(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       FALSE)
SELECTIONS_loc = paste0(ANALYSIS_loc, "/SELECTIONS")

cat("\nSetting working directory and listing its contents.\n")
setwd(paste0(PROJECT_loc))
getwd()
list.files()
```

A package-installation function.
```{r Function: installations, include = FALSE}
install.packages.auto <- function(x) { 
  x <- as.character(substitute(x)) 
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else { 
    # Update installed packages - this may mean a full upgrade of R, which in turn
    # may not be warrented. 
    # update.install.packages.auto(ask = FALSE) 
    eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"https://cloud.r-project.org/\")", x)))
  }
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else {
    if (!requireNamespace("BiocManager"))
      install.packages("BiocManager")
    # BiocManager::install() # this would entail updating installed packages, which in turned may not be warrented
    eval(parse(text = sprintf("BiocManager::install(\"%s\")", x)))
    eval(parse(text = sprintf("require(\"%s\")", x)))
  }
}
```

Load those packages.
```{r Setting: loading_packages, include = FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("MASS")
# install.packages.auto("Seurat") # latest version

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

install.packages.auto("haven")
install.packages.auto("sjlabelled")
install.packages.auto("sjPlot")
install.packages.auto("labelled")
install.packages.auto("tableone")

install.packages.auto("ggpubr")

```

We will create a datestamp and define the Utrecht Science Park Colour Scheme.
```{r Setting: Colors, include = FALSE}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
### 
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

#ggplot2 default color palette
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

### ----------------------------------------------------------------------------
```


```{r Analysis Functions}
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    tvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    R = summary(fit)$r.squared
    R.adj = summary(fit)$adj.r.squared
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
    
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("T-value...................:", round(tvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("R^2.......................:", round(R, 6), "\n")
    cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,9))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data...\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    zvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    dev <- fit$deviance
    nullDev <- fit$null.deviance
    modelN <- length(fit$fitted.values)
    R.l <- 1 - dev / nullDev
    R.cs <- 1 - exp(-(nullDev - dev) / modelN)
    R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("Z-value...................:", round(zvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
    cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
    cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}
```


# Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b).
While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.


## Objectives

Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.


## Methods

*Blood*

OLINK-platform 

- IL6: Interleukin 6. Entrez Gene: 3569. OLINK, plasma <!-- Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL] -->
- MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. OLINK, plasma <!-- Measured at the WKZ. Recalculated Luminex. [pg/mL] -->

> THESE DATA ARE NOT AVAILABLE YET

*Plaque*

Luminex-platform, measured by Luminex

- MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug]. Measured in two experiments, variables `MCP1` and `MCP1_pg_ug_2015`. We consider the latter the best possible measurement as this was corrected for plaque total protein concentration.
- IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug].
- IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug].

FACS platform

- IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]


# Loading data

## Clinical data

Loading Athero-Express clinical data.
```{r LoadAEDB}
require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))

head(AEDB)



```

#### Examine AEDB

We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is. 

There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/. 
```{r AEDB: describe}
AEDB %>% sjPlot::view_df(show.type = TRUE,
                         show.frq = TRUE,
                         show.prc = TRUE,
                         show.na = TRUE, 
                         max.len = TRUE, 
                         wrap.labels = 20,
                         verbose = FALSE, 
                         use.viewer = FALSE,
                         file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) 
```


## Fixing and creating variables

We need to be very strict in defining _symptoms._ Therefore we will fix a new variable that groups _symptoms_ at inclusion.

Coding of _symptoms_ is as follows:

- missing	-999	
- Asymptomatic	0	
- TIA	1	
- minor stroke	2	
- Major stroke	3	
- Amaurosis fugax	4	
- Four vessel disease	5	
- Vertebrobasilary TIA	7	
- Retinal infarction	8	
- Symptomatic, but aspecific symtoms	9
- Contralateral symptomatic occlusion	10	
- retinal infarction	11	
- armclaudication due to occlusion subclavian artery, CEA needed for bypass	12	
- retinal infarction + TIAs	13	
- Ocular ischemic syndrome	14	
- ischemisch glaucoom	15	
- subclavian steal syndrome	16	
- TGA	17

We will group as follows in `Symptoms.5G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13
3. Stroke > 2, 3
4. Ocular > 4, 14, 15
5. Retinal infarction > 8, 11
6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 
3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt2G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17


```{r FixSymptoms, message=FALSE, warning=FALSE}
# Fix symptoms

attach(AEDB)

AEDB$sympt[is.na(AEDB$sympt)] <- -999

# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

```

We will also fix the _plaquephenotypes_ variable.  

Coding of symptoms is as follows:

- missing	-999	
- not relevant -888
- fibrous	1	
- fibroatheromatous	2	
- atheromatous	3	


```{r FixPlaquePhenotypes, message=FALSE, warning=FALSE}

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

table(AEDB$OverallPlaquePhenotype)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

We will also fix the _diabetes_ status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.

```{r FixDiabetes, message=FALSE, warning=FALSE}
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

table(AEDB$DM.composite)

table(AEDB$DiabetesStatus)


# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```


We will also fix the _smoking_ status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire. 

- `diet801`: are you a smoker?
- `diet802`: did you smoke in the past?

We already have some variables indicating smoking status:

- `SmokingReported`: patient has reported to smoke.
- `SmokingYearOR`: smoking in the year of surgery?
- `SmokerCurrent`: currently smoking?



```{r FixSmoking, message=FALSE, warning=FALSE}
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))

cat("\n* Updated smoking status.\n")
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))

cat("\n* Comparing to 'SmokerCurrent'.\n")
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix the _alcohol_ status variable.


```{r FixAlcohol, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

table(AEDB$AlcoholUse)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on `CAD_history`, `Stroke_history`, and `Peripheral.interv`

```{r FixCAD_History, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)

table(AEDB$CAD_history)
table(AEDB$Stroke_history)
table(AEDB$Peripheral.interv)
table(AEDB$MedHx_CVD)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```




# Athero-Express Biobank Study

## Baseline characteristics

We are interested in the following variables at baseline.

- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
  - Never smokers
  - Ex-smokers
  - Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
  - Asymptomatic
  - Amaurosis fugax
  - TIA
  - Stroke
- eGFR (mL/min/1.73 m²)
- MCP-1 plaque levels (pg/mL) (LUMINEX based, two experiments `MCP1`, and `MCP1_pg_ug_2015`)
- MCP-1 serum levels (pg/mL) (OLINK based)


```{r Baseline AEDB: creation, include = FALSE}
cat("====================================================================================================\n")
cat("SELECTION THE SHIZZLE\n")

### Artery levels
# AEdata$Artery_summary: 
#           value                                                                                   label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1                                                                  carotid (left & right)
# USE - 2                                               femoral/iliac (left, right or both sides)
# NOT USE - 3                                               other carotid arteries (common, external)
# NOT USE - 4                                   carotid bypass and injury (left, right or both sides)
# NOT USE - 5                                                         aneurysmata (carotid & femoral)
# NOT USE - 6                                                                                   aorta
# NOT USE - 7                                            other arteries (renal, popliteal, vertebral)
# NOT USE - 8                        femoral bypass, angioseal and injury (left, right or both sides)

### AEdata$informedconsent
#           value                                                                                           label
# NOT USE - -999                                                                                         missing
# NOT USE - 0                                                                                        no, died
# USE - 1                                                                                             yes
# USE - 2                                                             yes, health treatment when possible
# USE - 3                                                                        yes, no health treatment
# USE - 4                                                yes, no health treatment, no commercial business
# NOT USE - 5                                                          yes, no tissue, no commerical business
# NOT USE - 6                      yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7                             yes, no questionnaires, no health treatment, no commercial business
# USE - 8                                          yes, no questionnaires, health treatment when possible
# NOT USE - 9                  yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10                               yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12                                                     yes, no questionnaires, no health treatment
# NOT USE - 13                                                             yes, no tissue, no health treatment
# NOT USE - 14                                                               yes, no tissue, no questionnaires
# NOT USE - 15                                                  yes, no tissue, health treatment when possible
# NOT USE - 16                                                                                  yes, no tissue
# USE - 17                                                                     yes, no commerical business
# USE - 18                                     yes, health treatment when possible, no commercial business
# USE - 19                                                    yes, no medical info, no commercial business
# USE - 20                                                                          yes, no questionnaires
# NOT USE - 21                         yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22                  yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23                                                                            yes, no medical info
# USE - 24                                                  yes, no questionnaires, no commercial business
# USE - 25                                    yes, no questionnaires, no health treatment, no medical info
# USE - 26                  yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27                                                      yes,  no health treatment, no medical info
# NOT USE - 28                                                                             no, doesn't want to
# NOT USE - 29                                                                              no, unable to sign
# NOT USE - 30                                                                                 no, no reaction
# NOT USE - 31                                                                                        no, lost
# NOT USE - 32                                                                                     no, too old
# NOT USE - 34                                            yes, no medical info, health treatment when possible
# NOT USE - 35                                             no (never asked for IC because there was no tissue)
# USE - 36                    yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37                                                                                    no, endpoint
# USE - 38                                                         wil niets invullen, wel alles gebruiken
# USE - 39                                           second informed concents: yes, no commercial business
# NOT USE - 40                                                                              nooit geincludeerd

cat("- sanity checking PRIOR to selection")
library(data.table)
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
ae.hospital <- ifelse(AEDB$Hospital == 1, "Antonius", "UMCU")
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
table(ae.gender, AEDB$Artery_summary, dnn = c("Sex", "Artery"))
# table(ae.gender, AEDB$informedconsent, dnn = c("Sex", "IC"))

rm(ae.gender, ae.hospital)

# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)

AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)

AEDB$TC_final <- as.numeric(AEDB$TC_final)
AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
AEDB$TG_final <- as.numeric(AEDB$TG_final)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)

AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)

# IL6, IL6R, MCP1 measurements
AEDB$IL6 <- as.numeric(AEDB$IL6) # FACS plaque
AEDB$IL6_pg_ug_2015 <- as.numeric(AEDB$IL6_pg_ug_2015) # LUMINEX plaque
AEDB$IL6R_pg_ug_2015 <- as.numeric(AEDB$IL6R_pg_ug_2015) # LUMINEX plaque
AEDB$MCP1 <- as.numeric(AEDB$MCP1) # LUMINEX plaque
AEDB$MCP1_pg_ug_2015 <- as.numeric(AEDB$MCP1_pg_ug_2015) # LUMINEX plaque
AEDB$hsCRP_plasma <- as.numeric(AEDB$hsCRP_plasma) # LUMINEX

require(labelled)
AEDB$ORyear <- to_factor(AEDB$ORyear)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)

AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)

AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)

AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$CAD_history <- to_factor(AEDB$CAD_history)
AEDB$PAOD <- to_factor(AEDB$PAOD)
AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)
AEDB$MedHx_CVD <- to_factor(AEDB$MedHx_CVD)


AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)


AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)

AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)

AEDB$informedconsent <- to_factor(AEDB$informedconsent)

AEDB.CEA <- subset(AEDB,
                    (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                       informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd" & 
                     !is.na(AsymptSympt2G))
# AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)
```

```{r}
cat("===========================================================================================\n")
cat("CREATE BASELINE TABLE\n")

# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "restenos", "stenose",
                   "MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6", "IL6_pg_ug_2015", "IL6R_pg_ug_2015",
                   "MCP1", "MCP1_pg_ug_2015")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

### All patients
Showing the baseline table of the whole Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients

Showing the baseline table of the CEA patients in the Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB CEA}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients with `MCP1_pg_ug_2015`

Showing the baseline table of the CEA patients in the Athero-Express Biobank with `MCP1_pg_ug_2015`.

```{r Baseline AEDB: Visualize subsetCEA}
AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015))

AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
```

### CEA patients with `MCP1_pg_ug_2015` and `MCP1`

Showing the baseline table of the CEA patients in the Athero-Express Biobank with `MCP1_pg_ug_2015` _and_ `MCP1`.

```{r Baseline AEDB: Visualize subsetCEA with MCP1}

AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) | !is.na(MCP1))

AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.combo, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
```

### CEA patients with plasma MCP1 levels

Showing the baseline table of the CEA patients in the Athero-Express Biobank with plasma MCP1 levels.

> NOT AVAILABLE YET

```{r Baseline AEDB: Visualize subsetCEA with serum MCP1}
AEDB.CEA.subset.serum <- subset(AEDB.CEA, !is.na(MCP1_plasma))

AEDB.CEA.subset.serum.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.serum, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
```


### CEA patients with plasma _and_ plaque MCP1 levels

Showing the baseline table of the CEA patients in the Athero-Express Biobank with both plasma and plaque MCP1 levels.

> NOT AVAILABLE YET

```{r Baseline AEDB: Visualize subsetCEA with serum MCP1 and plaque MCP1}
AEDB.CEA.subset.both <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) & !is.na(MCP1))

AEDB.CEA.subset.both.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.both, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
```

Writing the baseline table to Excel format. 
```{r Baseline AEDB: write}
# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
           AEDB.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
           AEDB.CEA.subset.AsymptSympt.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline_Sympt")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
           AEDB.CEA.subset.combo.tableOne,
           row.names = TRUE,
           col.names = TRUE,
           sheetName = "subsetAEDB_Baseline")

# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAserum.AsymptSympt.xlsx"),
#            AEDB.CEA.subset.serum.tableOne, 
#            row.names = TRUE, 
#            col.names = TRUE, 
#            sheetName = "subsetAEDB_Baseline_serum_Sympt")

```


## Data exploration

Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation. 

### MCP1 plaque levels 

We will explore the plaque levels. As noted above, we will use `MCP1_pg_ug_2015`.

```{r DataExploration: MCP1 plaque}

summary(AEDB.CEA$MCP1_pg_ug_2015)

do.call(rbind , by(AEDB.CEA$MCP1_pg_ug_2015, AEDB.CEA$AsymptSympt2G, summary))

```

```{r DataExploration: MCP1 plaque visual}
library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())

min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015

AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    # title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)
```


### MCP1 serum levels 

> NOT AVAILABLE YET

```{r DataExploration: MCP1 serum}

summary(AEDB.CEA$MCP1)

do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
do.call(rbind , by(AEDB.CEA.subset.serum$MCP1_pg_ug_2015, AEDB.CEA.subset.serum$AsymptSympt2G, summary))
do.call(rbind , by(AEDB.CEA.subset.serum$MCP1, AEDB.CEA.subset.serum$AsymptSympt2G, summary))

```

```{r DataExploration: MCP1 serum visual}
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())

min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1

AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 serum levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)

```

## Preliminary conclusion data exploration

In line with the previous work by [Marios Georgakis](https://www.ahajournals.org/doi/full/10.1161/CIRCRESAHA.119.315380){target="_blank"} we will apply _natural log transformation_ on all proteins and focus the analysis on MCP1 in serum and plaque.

# Analyses

The analyses are focused on three elements: 

1) plaque vulnerability phenotypes
2) clinical status at inclusion (symptoms)
3) secondary clinical outcome during three (3) years of follow-up

## Covariates & other variables

1.  Age (continuous in 1-year increment). [`Age`]
2.  Sex (male vs. female). [`Gender`]
3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [`Hypertension.composite`]
4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [`DiabetesStatus`]
5.  Smoking (current, ex-, never). [`SmokerStatus`]
6.  LDL-C levels (continuous). [`LDL_final`]
7.  Use of lipid-lowering drugs. [`Med.Statin.LLD`]
8.  Use of antiplatelet drugs. [`Med.all.antiplatelet`]
9.  eGFR (continuous). [`GFR_MDRD`]
10.	BMI (continuous). [`BMI`]
11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [`MedHx_CVD`] combination of [`CAD_history`, `Stroke_history`, `Peripheral.interv`]
12.	Level of stenosis (50-70% vs. 70-99%). [`stenose`]

## Models

We will analyze the data through four different models

- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

## A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque and serum MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:

- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)

*Continous traits*
```{r CrossSec: plaques - transformations and visualisations continuous}

# macrophages
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)

ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# smooth muscle cells
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)

ggpubr::gghistogram(AEDB.CEA, "SMC_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# vessel density
cat("Summary of data.\n")
summary(AEDB.CEA$vessel_density_averaged)

min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)

ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                    xlab = "natural log-transformed number", 
                    ggtheme = theme_minimal())

AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
```

*Binary traits*
```{r CrossSec: plaques - transformations and visualisations binary}

# calcification
cat("Summary of data.\n")
summary(AEDB.CEA$Calc.bin)
contrasts(AEDB.CEA$Calc.bin)

AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CalcificationPlaque)) %>%
  group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())
rm(df)

# collagen
cat("Summary of data.\n")
summary(AEDB.CEA$Collagen.bin)
contrasts(AEDB.CEA$Collagen.bin)

AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CollagenPlaque)) %>%
  group_by(Gender, CollagenPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())
rm(df)

# fat 10%
cat("Summary of data.\n")
summary(AEDB.CEA$Fat.bin_10)
contrasts(AEDB.CEA$Fat.bin_10)

AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)

df <- AEDB.CEA %>%
  filter(!is.na(Fat10Perc)) %>%
  group_by(Gender, Fat10Perc) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())
rm(df)

# IPH
cat("Summary of data.\n")
summary(AEDB.CEA$IPH.bin)
contrasts(AEDB.CEA$IPH.bin)

AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)

df <- AEDB.CEA %>%
  filter(!is.na(IPH)) %>%
  group_by(Gender, IPH) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())
rm(df)

# Symptoms
cat("Summary of data.\n")
summary(AEDB.CEA$AsymptSympt)
contrasts(AEDB.CEA$AsymptSympt)

AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)

df <- AEDB.CEA %>%
  filter(!is.na(AsymptSympt)) %>%
  group_by(Gender, AsymptSympt) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())
rm(df)

```

In this section we make some variables to assist with analysis.
```{r CrossSec: plaques - setup regression }
AEDB.CEA.samplesize = nrow(AEDB.CEA)
# TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
# TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")
# TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_rank")
TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank")

# TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN") 
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.	BMI (continuous). [BMI]
# 11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.	Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

COVARIATES_M1 = c("Age", "Gender")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")

# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

# COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
# COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")

```

### Model 1

In this model we correct for _Age_ and _Gender_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque and serum MCP1, IL6, and IL6R levels and the 'clinical status' of the plaque in terms of presence of patients' symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic

### Model 1

In this model we correct for _Age_, and _Gender_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

```{r CrossSec: symptoms - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Binary plaque traits


```{r CrossSec: symptoms - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                MedHx_CVD + + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque and serum MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period. 

The _primary outcome_ is defined as "a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death", i.e. major adverse cardiovascular events (MACE). Variable: `epmajor.3years`, these include:
- myocardial infarction (MI)
- cerebral infarction (CVA/stroke)
- cardiovascular death (exact cause to be investigated)
- cerebral bleeding (CVA/stroke)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..

The _secondary outcomes_ will be 

- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: `epstroke.3years`, these include:
  - cerebral infarction (CVA/stroke)
  - cerebral bleeding (CVA/stroke)
  - fatal cerebral infarction
  - fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: `epcoronary.3years`, these include:
  - myocardial infarction (MI)
  - coronary angioplasty (PCI/PTCA)
  - cardiovascular death (exact cause to be investigated)
  - coronary bypass (CABG)
  - fatal myocardial infarction (MI)
  - sudden death.
- cardiovascular death - variable: `epcvdeath.3years`, these include:
  - cardiovascular death (exact cause to be investigated)
  - fatal myocardial infarction (MI)
  - fatal cerebral infarction
  - fatal cerebral bleeding
  - sudden death
  - fatal heart failure
  - fatal aneurysm rupture
  - other cardiovascular death..

### 30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up 'time-to-event' variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up. 

```{r Calculate new FU cut-offs: maximum FU}
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)


```

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

- MACE (`epmajor.3years`)
- Stroke (`epstroke.3years`)
- Coronary events (`epcoronary.3years`)
- Cardiovascular death (`epcvdeath.3years`)


```{r Calculate new FU cut-offs: times}
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

```


```{r Cox-regressions: new times}

attach(AEDB)
AEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

attach(AEDB.CEA)
AEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB.CEA)

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)



```

### Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.
```{r Cox-regressions: General}
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
  cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(protein, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", protein, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}


```


### Cox regressions

Let's perform the actual Cox-regressions. We will apply a couple of models: 

- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

#### 3 years follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 30-days follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 90-days follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```



# Correlations

## All biomarkers
We correlated serum and plaque levels of the biomarkers.

```{r CrossSampleType Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_pg_ug_2015_rank",
                                               TRAITS.BIN, TRAITS.CON.RANK)
                                    )


AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:

# ggpairs(AEDB.CEA, 
#         columns = c("MCP1_rank", "MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK), 
#         columnLabels = c("MCP1 (serum)", "MCP1", 
#                          "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
#         method = c("spearman"),
#         # ggplot2::aes(colour = Gender),
#         progress = FALSE) 

ggpairs(AEDB.CEA,
        columns = c("MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK),
        columnLabels = c("MCP1",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE)

```

## Circulating MCP1

Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:

1. A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
2. Associations of circulating MCP-1 levels with plaque vulnerability characteristics
3. Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
4. Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.

> NOT AVAILABLE YET

```{r MCP1 Serum Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_rank", 
                                     TRAITS.BIN, TRAITS.CON.RANK, 
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers_serum.rank <- round(cor(AEDB.CEA.matrix.serum.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_serum_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_serum_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}


corr_biomarkers_serum.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum.rank, corr_biomarkers_serum_p.rank))
DT::datatable(corr_biomarkers_serum.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AEDB.CEA.matrix.serum.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA,
        columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"), 
        columnLabels = c("MCP1 (serum)", 
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 
```

# Additional figures

## Age and sex
We want to create per-age-group figures. 

- Box and Whisker plot for MCP-1 plaque levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Box and Whisker plot for MCP-1 serum levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Scatter plot of the correlation and regression line between MCP-1 levels in plaque (y axis) and serum (x axis).


```{r AgeGroups}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AEDB.CEA$AgeGroup, AEDB.CEA$Gender)
table(AEDB.CEA$AgeGroupSex)

```

Now we can draw some graphs of serum/plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per AgeGroup per Sex, ranked}

# ?ggpubr::ggboxplot()

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
```

#### MCP1 serum levels

> NOT AVAILABLE YET

```{r MCP1 per AgeGroup per Sex, ranked serum}
# compare_means(MCP1_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("Gender"),
                  y = "MCP1_rank",
                  xlab = "Gender",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("AgeGroup"),
                  y = "MCP1_rank",
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per AgeGroup per Sex}

# ?ggpubr::ggboxplot()
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```

### MCP1 serum levels

> NOT AVAILABLE YET

```{r MCP1 per AgeGroup per Sex, serum}
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1", 
                  xlab = "Gender",
                  ylab = "MCP1 serum [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 serum [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```

## Circulating vs. plaque MCP1 levels

We will also make a nice correlation plot between serum and plaque MCP1 levels.

> NOT AVAILABLE YET

```{r MCP1 correlations, serum vs. plaque}

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015",
                  y = "MCP1", 
                  xlab = "MCP1 plaque [pg/ug]",
                  ylab = "MCP1 serum [pg/mL]",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(8,750))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015_rank",
                  y = "MCP1_rank", 
                  xlab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(2,3))

```

## Plaque vs. plaque MCP1 levels
We will also make a nice correlation plot between the two experiments of plaque MCP1 levels. 

```{r MCP1 correlations, plaque vs. plaque}
AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1", 
                  y = "MCP1_pg_ug_2015",
                  xlab = "MCP1 plaque [pg/mL] (exp. no. 1)",
                  ylab = "MCP1 plaque [pg/ug] (exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(8,750))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_rank", 
                  y = "MCP1_pg_ug_2015_rank",
                  xlab = "MCP1 plaque [pg/mL]\n(INRT,  exp. no. 1)",
                  ylab = "MCP1 plaque [pg/ug]\n(INRT,  exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(2,3))

```


## Symptoms
We want to create per-symptom figures. 


```{r SymptomGroups}
library(dplyr)

table(AEDB.CEA$AgeGroup, AEDB.CEA$AsymptSympt2G)
table(AEDB.CEA$Gender, AEDB.CEA$AsymptSympt2G)
table(AEDB.CEA$AsymptSympt2G)

```

Now we can draw some graphs of serum/plaque MCP1 levels per symptom group.
```{r MCP1 per SymptomGroups}

# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_pg_ug_2015_rank",
                  title = "MCP1 plaque [pg/ug] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 plaque [pg/ug]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
```

```{r MCP1 per SymptomGroups, serum}
p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_rank",
                  title = "MCP1 serum [pg/mL] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 serum [pg/mL]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
rm(p1)
```

## Forest plots

We would also like to visualize the multivariable analyses results.
```{r load model data}
library(ggplot2)
library(openxlsx)
model1_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"))
model2_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"))
model1_mcp1$model <- "univariate"
model2_mcp1$model <- "multivariate"

models_mcp1 <- rbind(model1_mcp1, model2_mcp1)
models_mcp1

```

```{r forestplot plaque}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]))

fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
  geom_pointrange() + 
  geom_hline(yintercept=1, lty=2) +  # add a dotted line at x=1 after flip
  coord_flip() +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  theme(text = element_text(size=14)) +
  ggtitle("Plaque MCP-1 levels (1 SD increment)") +
  theme_minimal()  # use a white background
print(fp)
rm(fp)
```


```{r forestplot serum}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_rank"]))

fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
  geom_pointrange() + 
  geom_hline(yintercept=1, lty=2) +  # add a dotted line at x=1 after flip
  coord_flip() +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  theme(text = element_text(size=14)) +
  ggtitle("Serum MCP-1 levels (1 SD increment)") +
  theme_minimal()  # use a white background
print(fp)
rm(fp)
```

## MCP1 vs. cytokines plaque levels 

We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:

- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay. 

- MMP2
- MMP8
- MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the MCP1 plaque levels.


```{r MCP1 vs Cytokines IRN}
cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")

# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"


proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))


# make variables numerics()
AEDB.CEA <- AEDB.CEA %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
  
for(PROTEIN in 1:length(proteins_of_interest)){

  # UCORBIOGSAqc$Z <- NULL
  var.temp.rank = proteins_of_interest_rank[PROTEIN]
  var.temp = proteins_of_interest[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  # AEDB.CEA <-  AEDB.CEA %>% mutate(AEDB.CEA[,var.temp] == replace(AEDB.CEA[,var.temp], AEDB.CEA[,var.temp]==0, NA))

  AEDB.CEA[,var.temp][AEDB.CEA[,var.temp]==0.000000]=NA

  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AEDB.CEA[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AEDB.CEA[,var.temp])),").\n"))
  
  AEDB.CEA <- AEDB.CEA %>%
      mutate_at(vars(var.temp), 
        # list(Z = ~ (AEDB.CEA[,var.temp] - mean(AEDB.CEA[,var.temp], na.rm = TRUE))/sd(AEDB.CEA[,var.temp], na.rm = TRUE))
        list(RANK = ~ qnorm((rank(AEDB.CEA[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA[,var.temp]))))
      )
  # str(UCORBIOGSAqc$Z)
  cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
  AEDB.CEA[,var.temp.rank] <- NULL
  names(AEDB.CEA)[names(AEDB.CEA) == "RANK"] <- var.temp.rank
}

# rm(var.temp, var.temp.rank)

```

We will just visualize these transformations.

```{r MCP1 vs Cytokines Histograms}
proteins_of_interest_rank_mcp1 <- c("MCP1_pg_ug_2015_rank", proteins_of_interest_rank)

proteins_of_interest_mcp1 <- c("MCP1_pg_ug_2015", proteins_of_interest)

for(PROTEIN in proteins_of_interest_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "",
                    ggtheme = theme_minimal())
  print(p1)
  
}


for(PROTEIN in proteins_of_interest_rank_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "inverse-normal transformation",
                    ggtheme = theme_minimal())
  print(p1)
  
}
  
```

```{r balbla}
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c(proteins_of_interest_rank_mcp1)
                                    )

# str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

rename_proteins_of_interest_mcp1 <- c("MCP1 (L, exp2)", 
                                    "IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", 
                                    "IL13 (L)", "IL21 (L)", 
                                    "INFG", "TNFA", "MIF (L)", 
                                    "MCP1 (L, exp1)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)", 
                                    "Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)", 
                                    "OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (r)", "MMP8 (r)", "MMP9 (r)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           tl.cex = 6,
           # xlab = c("MCP1"),
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

```



# Session information

------

    Version:      v1.0.8
    Last update:  2020-06-25
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse MCP1 from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_
    * analysis on serum
    
    _S_
    * prettify forest plot
    
    _C_
    
    
    _W_
    
    
    **Changes log**
    * v1.0.8 Fixed error in MCP1 serum analysis. It turns out the `MCP1` and `MCP1_pg_ug_2015` variables are _both_ measured in plaque, in two separate experiments, exp. no. 1 and exp. no. 2, respectively. 
    * v1.0.7 Fixed the per Age-group MCP1 Box plots. Added correlations with other cytokines in plaques.
    * v1.0.6 Only analyses and figures that end up in the final manuscript.
    * v1.0.5 Update with 30- and 90-days survival.
    * v1.0.4 Updated with Cox-regressions.
    * v1.0.3 Included more models.
    * v1.0.2 Bugs fixed.
    * v1.0.1 Extended with linear and logistic regressions
    * v1.0.0 Inital version
    

------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))
```

------
<sup>&copy; 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup>
------

